507 Commits

Author SHA1 Message Date
Jeremy Reizenstein
f25af96959 vert_align for Pointclouds object
Reviewed By: gkioxari

Differential Revision: D21088730

fbshipit-source-id: f8c125ac8c8009d45712ae63237ca64acf1faf45
2020-04-17 10:39:43 -07:00
Roman Shapovalov
04d8bf6a43 Efficient PnP.
Summary:
Efficient PnP algorithm to fit 2D to 3D correspondences under perspective assumption.

Benchmarked both variants of nullspace and pick one; SVD takes 7 times longer in the 100K points case.

Reviewed By: davnov134, gkioxari

Differential Revision: D20095754

fbshipit-source-id: 2b4519729630e6373820880272f674829eaed073
2020-04-17 07:44:16 -07:00
David Novotny
7788a38050 Camera inheritance + unprojections
Summary: Made a CameraBase class. Added `unproject_points` method for each camera class.

Reviewed By: nikhilaravi

Differential Revision: D20373602

fbshipit-source-id: 7e3da5ae420091b5fcab400a9884ef29ad7a7343
2020-04-17 04:38:14 -07:00
David Novotny
365945b1fd Pointcloud normals estimation.
Summary: Estimates normals of a point cloud.

Reviewed By: gkioxari

Differential Revision: D20860182

fbshipit-source-id: 652ec2743fa645e02c01ffa37c2971bf27b89cef
2020-04-16 18:36:19 -07:00
David Novotny
8abbe22ffb ICP - point-to-point version
Summary:
The iterative closest point algorithm - point-to-point version.

Output of `bm_iterative_closest_point`:
Argument key: `batch_size dim n_points_X n_points_Y use_pointclouds`

```
Benchmark                                         Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
IterativeClosestPoint_1_3_100_100_False              107569          111323              5
IterativeClosestPoint_1_3_100_1000_False             118972          122306              5
IterativeClosestPoint_1_3_1000_100_False             108576          110978              5
IterativeClosestPoint_1_3_1000_1000_False            331836          333515              2
IterativeClosestPoint_1_20_100_100_False             134387          137842              4
IterativeClosestPoint_1_20_100_1000_False            149218          153405              4
IterativeClosestPoint_1_20_1000_100_False            414248          416595              2
IterativeClosestPoint_1_20_1000_1000_False           374318          374662              2
IterativeClosestPoint_10_3_100_100_False             539852          539852              1
IterativeClosestPoint_10_3_100_1000_False            752784          752784              1
IterativeClosestPoint_10_3_1000_100_False           1070700         1070700              1
IterativeClosestPoint_10_3_1000_1000_False          1164020         1164020              1
IterativeClosestPoint_10_20_100_100_False            374548          377337              2
IterativeClosestPoint_10_20_100_1000_False           472764          476685              2
IterativeClosestPoint_10_20_1000_100_False          1457175         1457175              1
IterativeClosestPoint_10_20_1000_1000_False         2195820         2195820              1
IterativeClosestPoint_1_3_100_100_True               110084          115824              5
IterativeClosestPoint_1_3_100_1000_True              142728          147696              4
IterativeClosestPoint_1_3_1000_100_True              212966          213966              3
IterativeClosestPoint_1_3_1000_1000_True             369130          375114              2
IterativeClosestPoint_10_3_100_100_True              354615          355179              2
IterativeClosestPoint_10_3_100_1000_True             451815          452704              2
IterativeClosestPoint_10_3_1000_100_True             511833          511833              1
IterativeClosestPoint_10_3_1000_1000_True            798453          798453              1
--------------------------------------------------------------------------------
```

Reviewed By: shapovalov, gkioxari

Differential Revision: D19909952

fbshipit-source-id: f77fadc88fb7c53999909d594114b182ee2a3def
2020-04-16 14:02:16 -07:00
Nikhila Ravi
b530b0af32 lint fixes
Summary: Resolved trailing whitespace warnings.

Reviewed By: gkioxari

Differential Revision: D21023982

fbshipit-source-id: 14ea2ca372c13cfa987acc260264ca99ce44c461
2020-04-15 21:58:59 -07:00
Nikhila Ravi
3794f6753f remove nearest_neighbors
Summary: knn is more general and faster than the nearest_neighbor code, so remove the latter.

Reviewed By: gkioxari

Differential Revision: D20816424

fbshipit-source-id: 75d6c44d17180752d0c9859814bbdf7892558158
2020-04-15 20:51:41 -07:00
Nikhila Ravi
790eb8c402 Chamfer for Pointclouds object
Summary:
Allow Pointclouds objects and heterogenous data to be provided for Chamfer loss. Remove "none" as an option for point_reduction because it doesn't make sense and in the current implementation is effectively the same as "sum".

Possible improvement: create specialised operations for sum and cosine_similarity of padded tensors, to avoid having to create masks. sum would be useful elsewhere.

Reviewed By: gkioxari

Differential Revision: D20816301

fbshipit-source-id: 0f32073210225d157c029d80de450eecdb64f4d2
2020-04-15 14:10:45 -07:00
Siddhant Ranade
677b0bd5ae SfMPerspectiveCameras projection matrix bug fix (#148)
Summary:
Fixed a bug in creating the projection matrix for the SfMPerspectiveCameras class. Also fixed the corresponding test in test_cameras.py.

The p0x, p0y are 2D coordinates for the principal point in the NDCS, and therefore should be added AFTER the perspective z divide. I.e. we expect

<a href="https://www.codecogs.com/eqnedit.php?latex=\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;&plus;&space;p_x\&space;f_yY/Z&space;&plus;&space;p_y\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;&plus;&space;p_xZ\&space;f_yY&space;&plus;&space;p_yZ\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;&plus;&space;p_x\&space;f_yY/Z&space;&plus;&space;p_y\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;&plus;&space;p_xZ\&space;f_yY&space;&plus;&space;p_yZ\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" title="\begin{bmatrix} x\ y\ z \end{bmatrix}_{\text{NDCS}} = \begin{bmatrix} f_xX/Z + p_x\ f_yY/Z + p_y\ 1 / Z\ \end{bmatrix} = \text{normalize}\left( \begin{bmatrix} f_xX + p_xZ\ f_yY + p_yZ\ 1\ Z \end{bmatrix} \right)" /></a>

The current behavior is

<a href="https://www.codecogs.com/eqnedit.php?latex=\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;&plus;&space;p_x/Z\&space;f_yY/Z&space;&plus;&space;p_y/Z\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;&plus;&space;p_x\&space;f_yY&space;&plus;&space;p_y\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;&plus;&space;p_x/Z\&space;f_yY/Z&space;&plus;&space;p_y/Z\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;&plus;&space;p_x\&space;f_yY&space;&plus;&space;p_y\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" title="\begin{bmatrix} x\ y\ z \end{bmatrix}_{\text{NDCS}} = \begin{bmatrix} f_xX/Z + p_x/Z\ f_yY/Z + p_y/Z\ 1 / Z\ \end{bmatrix} = \text{normalize}\left( \begin{bmatrix} f_xX + p_x\ f_yY + p_y\ 1\ Z \end{bmatrix} \right)" /></a>

which is incorrect.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/148

Reviewed By: gkioxari

Differential Revision: D21039003

Pulled By: davnov134

fbshipit-source-id: 3e19ac22adbcc39b731ae14052a72fd4ddda2af5
2020-04-15 10:20:43 -07:00
Georgia Gkioxari
b2b0c5a442 knn autograd
Summary:
Adds knn backward to return `grad_pts1` and `grad_pts2`. Adds `knn_gather` to return the nearest neighbors in pts2.

The BM tests include backward pass and are ran on an M40.
```
Benchmark                               Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
KNN_SQUARE_32_256_128_3_24_cpu              39558           43485             13
KNN_SQUARE_32_256_128_3_24_cuda:0            1080            1404            463
KNN_SQUARE_32_256_512_3_24_cpu              81950           85781              7
KNN_SQUARE_32_256_512_3_24_cuda:0            1519            1641            330
--------------------------------------------------------------------------------

Benchmark                               Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
KNN_RAGGED_32_256_128_3_24_cpu              13798           14650             37
KNN_RAGGED_32_256_128_3_24_cuda:0            1576            1713            318
KNN_RAGGED_32_256_512_3_24_cpu              31255           32210             16
KNN_RAGGED_32_256_512_3_24_cuda:0            2024            2162            248
--------------------------------------------------------------------------------
```

Reviewed By: jcjohnson

Differential Revision: D20945556

fbshipit-source-id: a16f616029c6b5f8c2afceb5f2bc12c5c20d2f3c
2020-04-14 17:22:56 -07:00
Georgia Gkioxari
487d4d6607 point mesh distances
Summary:
Implementation of point to mesh distances. The current diff contains two types:
(a) Point to Edge
(b) Point to Face

```

Benchmark                                       Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
POINT_MESH_EDGE_4_100_300_5000_cuda:0                2745            3138            183
POINT_MESH_EDGE_4_100_300_10000_cuda:0               4408            4499            114
POINT_MESH_EDGE_4_100_3000_5000_cuda:0               4978            5070            101
POINT_MESH_EDGE_4_100_3000_10000_cuda:0              9076            9187             56
POINT_MESH_EDGE_4_1000_300_5000_cuda:0               1411            1487            355
POINT_MESH_EDGE_4_1000_300_10000_cuda:0              4829            5030            104
POINT_MESH_EDGE_4_1000_3000_5000_cuda:0              7539            7620             67
POINT_MESH_EDGE_4_1000_3000_10000_cuda:0            12088           12272             42
POINT_MESH_EDGE_8_100_300_5000_cuda:0                3106            3222            161
POINT_MESH_EDGE_8_100_300_10000_cuda:0               8561            8648             59
POINT_MESH_EDGE_8_100_3000_5000_cuda:0               6932            7021             73
POINT_MESH_EDGE_8_100_3000_10000_cuda:0             24032           24176             21
POINT_MESH_EDGE_8_1000_300_5000_cuda:0               5272            5399             95
POINT_MESH_EDGE_8_1000_300_10000_cuda:0             11348           11430             45
POINT_MESH_EDGE_8_1000_3000_5000_cuda:0             17478           17683             29
POINT_MESH_EDGE_8_1000_3000_10000_cuda:0            25961           26236             20
POINT_MESH_EDGE_16_100_300_5000_cuda:0               8244            8323             61
POINT_MESH_EDGE_16_100_300_10000_cuda:0             18018           18071             28
POINT_MESH_EDGE_16_100_3000_5000_cuda:0             19428           19544             26
POINT_MESH_EDGE_16_100_3000_10000_cuda:0            44967           45135             12
POINT_MESH_EDGE_16_1000_300_5000_cuda:0              7825            7937             64
POINT_MESH_EDGE_16_1000_300_10000_cuda:0            18504           18571             28
POINT_MESH_EDGE_16_1000_3000_5000_cuda:0            65805           66132              8
POINT_MESH_EDGE_16_1000_3000_10000_cuda:0           90885           91089              6
--------------------------------------------------------------------------------

Benchmark                                       Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
POINT_MESH_FACE_4_100_300_5000_cuda:0                1561            1685            321
POINT_MESH_FACE_4_100_300_10000_cuda:0               2818            2954            178
POINT_MESH_FACE_4_100_3000_5000_cuda:0              15893           16018             32
POINT_MESH_FACE_4_100_3000_10000_cuda:0             16350           16439             31
POINT_MESH_FACE_4_1000_300_5000_cuda:0               3179            3278            158
POINT_MESH_FACE_4_1000_300_10000_cuda:0              2353            2436            213
POINT_MESH_FACE_4_1000_3000_5000_cuda:0             16262           16336             31
POINT_MESH_FACE_4_1000_3000_10000_cuda:0             9334            9448             54
POINT_MESH_FACE_8_100_300_5000_cuda:0                4377            4493            115
POINT_MESH_FACE_8_100_300_10000_cuda:0               9728            9822             52
POINT_MESH_FACE_8_100_3000_5000_cuda:0              26428           26544             19
POINT_MESH_FACE_8_100_3000_10000_cuda:0             42238           43031             12
POINT_MESH_FACE_8_1000_300_5000_cuda:0               3891            3982            129
POINT_MESH_FACE_8_1000_300_10000_cuda:0              5363            5429             94
POINT_MESH_FACE_8_1000_3000_5000_cuda:0             20998           21084             24
POINT_MESH_FACE_8_1000_3000_10000_cuda:0            39711           39897             13
POINT_MESH_FACE_16_100_300_5000_cuda:0               5955            6001             84
POINT_MESH_FACE_16_100_300_10000_cuda:0             12082           12144             42
POINT_MESH_FACE_16_100_3000_5000_cuda:0             44996           45176             12
POINT_MESH_FACE_16_100_3000_10000_cuda:0            73042           73197              7
POINT_MESH_FACE_16_1000_300_5000_cuda:0              8292            8374             61
POINT_MESH_FACE_16_1000_300_10000_cuda:0            19442           19506             26
POINT_MESH_FACE_16_1000_3000_5000_cuda:0            36059           36194             14
POINT_MESH_FACE_16_1000_3000_10000_cuda:0           64644           64822              8
--------------------------------------------------------------------------------
```

Reviewed By: jcjohnson

Differential Revision: D20590462

fbshipit-source-id: 42a39837b514a546ac9471bfaff60eefe7fae829
2020-04-11 00:21:24 -07:00
Jeremy Reizenstein
01b5f7b228 heterogenous KNN
Summary: Interface and working implementation of ragged KNN. Benchmarks (which aren't ragged) haven't slowed. New benchmark shows that ragged is faster than non-ragged of the same shape.

Reviewed By: jcjohnson

Differential Revision: D20696507

fbshipit-source-id: 21b80f71343a3475c8d3ee0ce2680f92f0fae4de
2020-04-07 01:47:37 -07:00
Jeremy Reizenstein
29b9c44c0a Allow conda's generated files.
Summary: The conda build process generates some files of its own, which we don't want to catch in our test for copyright notices.

Reviewed By: nikhilaravi, patricklabatut

Differential Revision: D20868566

fbshipit-source-id: 76a786a3eb9a674d59e630cc06f346e8b82258a4
2020-04-06 10:03:57 -07:00
Jeremy Reizenstein
b87058c62a fix recent lint
Summary: lint clean again

Reviewed By: patricklabatut

Differential Revision: D20868775

fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9
2020-04-06 06:41:00 -07:00
David Novotny
90dc7a0856 Initialization of Transform3D with a custom matrix.
Summary:
Allows to initialize a Transform3D object with a batch of user-defined transformation matrices:
```
t = Transform3D(matrix=torch.randn(2, 4, 4))
```

Reviewed By: nikhilaravi

Differential Revision: D20693475

fbshipit-source-id: dccc49b2ca4c19a034844c63463953ba8f52c1bc
2020-04-05 14:44:27 -07:00
Roman Shapovalov
e37085d999 Weighted Umeyama.
Summary:
1. Introduced weights to Umeyama implementation. This will be needed for weighted ePnP but is useful on its own.
2. Refactored to use the same code for the Pointclouds mask and passed weights.
3. Added test cases with random weights.
4. Fixed a bug in tests that calls the function with 0 points (fails randomly in Pytorch 1.3, will be fixed in the next release: https://github.com/pytorch/pytorch/issues/31421 ).

Reviewed By: gkioxari

Differential Revision: D20070293

fbshipit-source-id: e9f549507ef6dcaa0688a0f17342e6d7a9a4336c
2020-04-03 02:59:11 -07:00
David Novotny
e5b1d6d3a3 Umeyama
Summary:
Umeyama estimates a rigid motion between two sets of corresponding points.

Benchmark output for `bm_points_alignment`

```
Arguments key: [<allow_reflection>_<batch_size>_<dim>_<estimate_scale>_<n_points>_<use_pointclouds>]
Benchmark                                                        Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
CorrespodingPointsAlignment_True_1_3_True_100_False                   7382            9833             68
CorrespodingPointsAlignment_True_1_3_True_10000_False                 8183           10500             62
CorrespodingPointsAlignment_True_1_3_False_100_False                  7301            9263             69
CorrespodingPointsAlignment_True_1_3_False_10000_False                7945            9746             64
CorrespodingPointsAlignment_True_1_20_True_100_False                 13706           41623             37
CorrespodingPointsAlignment_True_1_20_True_10000_False               11044           33766             46
CorrespodingPointsAlignment_True_1_20_False_100_False                 9908           28791             51
CorrespodingPointsAlignment_True_1_20_False_10000_False               9523           18680             53
CorrespodingPointsAlignment_True_10_3_True_100_False                 29585           32026             17
CorrespodingPointsAlignment_True_10_3_True_10000_False               29626           36324             18
CorrespodingPointsAlignment_True_10_3_False_100_False                26013           29253             20
CorrespodingPointsAlignment_True_10_3_False_10000_False              25000           33820             20
CorrespodingPointsAlignment_True_10_20_True_100_False                40955           41592             13
CorrespodingPointsAlignment_True_10_20_True_10000_False              42087           42393             12
CorrespodingPointsAlignment_True_10_20_False_100_False               39863           40381             13
CorrespodingPointsAlignment_True_10_20_False_10000_False             40813           41699             13
CorrespodingPointsAlignment_True_100_3_True_100_False               183146          194745              3
CorrespodingPointsAlignment_True_100_3_True_10000_False             213789          231466              3
CorrespodingPointsAlignment_True_100_3_False_100_False              177805          180796              3
CorrespodingPointsAlignment_True_100_3_False_10000_False            184963          185695              3
CorrespodingPointsAlignment_True_100_20_True_100_False              347181          347325              2
CorrespodingPointsAlignment_True_100_20_True_10000_False            363259          363613              2
CorrespodingPointsAlignment_True_100_20_False_100_False             351769          352496              2
CorrespodingPointsAlignment_True_100_20_False_10000_False           375629          379818              2
CorrespodingPointsAlignment_False_1_3_True_100_False                 11155           13770             45
CorrespodingPointsAlignment_False_1_3_True_10000_False               10743           13938             47
CorrespodingPointsAlignment_False_1_3_False_100_False                 9578           11511             53
CorrespodingPointsAlignment_False_1_3_False_10000_False               9549           11984             53
CorrespodingPointsAlignment_False_1_20_True_100_False                13809           14183             37
CorrespodingPointsAlignment_False_1_20_True_10000_False              14084           15082             36
CorrespodingPointsAlignment_False_1_20_False_100_False               12765           14177             40
CorrespodingPointsAlignment_False_1_20_False_10000_False             12811           13096             40
CorrespodingPointsAlignment_False_10_3_True_100_False                28823           39384             18
CorrespodingPointsAlignment_False_10_3_True_10000_False              27135           27525             19
CorrespodingPointsAlignment_False_10_3_False_100_False               26236           28980             20
CorrespodingPointsAlignment_False_10_3_False_10000_False             42324           45123             12
CorrespodingPointsAlignment_False_10_20_True_100_False              723902          723902              1
CorrespodingPointsAlignment_False_10_20_True_10000_False            220007          252886              3
CorrespodingPointsAlignment_False_10_20_False_100_False              55593           71636              9
CorrespodingPointsAlignment_False_10_20_False_10000_False            44419           71861             12
CorrespodingPointsAlignment_False_100_3_True_100_False              184768          185199              3
CorrespodingPointsAlignment_False_100_3_True_10000_False            198657          213868              3
CorrespodingPointsAlignment_False_100_3_False_100_False             224598          309645              3
CorrespodingPointsAlignment_False_100_3_False_10000_False           197863          202002              3
CorrespodingPointsAlignment_False_100_20_True_100_False             293484          309459              2
CorrespodingPointsAlignment_False_100_20_True_10000_False           327253          366644              2
CorrespodingPointsAlignment_False_100_20_False_100_False            420793          422194              2
CorrespodingPointsAlignment_False_100_20_False_10000_False          462634          485542              2
CorrespodingPointsAlignment_True_1_3_True_100_True                    7664            9909             66
CorrespodingPointsAlignment_True_1_3_True_10000_True                  7190            8366             70
CorrespodingPointsAlignment_True_1_3_False_100_True                   6549            8316             77
CorrespodingPointsAlignment_True_1_3_False_10000_True                 6534            7710             77
CorrespodingPointsAlignment_True_10_3_True_100_True                  29052           32940             18
CorrespodingPointsAlignment_True_10_3_True_10000_True                30526           33453             17
CorrespodingPointsAlignment_True_10_3_False_100_True                 28708           32993             18
CorrespodingPointsAlignment_True_10_3_False_10000_True               30630           35973             17
CorrespodingPointsAlignment_True_100_3_True_100_True                264909          320820              3
CorrespodingPointsAlignment_True_100_3_True_10000_True              310902          322604              2
CorrespodingPointsAlignment_True_100_3_False_100_True               246832          250634              3
CorrespodingPointsAlignment_True_100_3_False_10000_True             276006          289061              2
CorrespodingPointsAlignment_False_1_3_True_100_True                  11421           13757             44
CorrespodingPointsAlignment_False_1_3_True_10000_True                11199           12532             45
CorrespodingPointsAlignment_False_1_3_False_100_True                 11474           15841             44
CorrespodingPointsAlignment_False_1_3_False_10000_True               10384           13188             49
CorrespodingPointsAlignment_False_10_3_True_100_True                 36599           47340             14
CorrespodingPointsAlignment_False_10_3_True_10000_True               40702           50754             13
CorrespodingPointsAlignment_False_10_3_False_100_True                41277           52149             13
CorrespodingPointsAlignment_False_10_3_False_10000_True              34286           37091             15
CorrespodingPointsAlignment_False_100_3_True_100_True               254991          258578              2
CorrespodingPointsAlignment_False_100_3_True_10000_True             257999          261285              2
CorrespodingPointsAlignment_False_100_3_False_100_True              247511          248693              3
CorrespodingPointsAlignment_False_100_3_False_10000_True            251807          263865              3
```

Reviewed By: gkioxari

Differential Revision: D19808389

fbshipit-source-id: 83305a58627d2fc5dcaf3c3015132d8148f28c29
2020-04-02 14:46:51 -07:00
Patrick Labatut
83feed56a0 Fix saving / loading empty PLY meshes
Summary:
Similar to D20392526, PLY files without vertices or faces should be allowed:
- a PLY with only vertices can represent a point cloud
- a PLY without any vertex or face is just empty
- a PLY with faces referencing inexistent vertices has invalid data

Reviewed By: gkioxari

Differential Revision: D20400330

fbshipit-source-id: 35a5f072603fd221f382c7faad5f37c3e0b49bb1
2020-04-01 04:51:20 -07:00
Jeremy Reizenstein
b64fe51360 join_meshes_as_batch
Summary: rename join_meshes to join_meshes_as_batch.

Reviewed By: nikhilaravi

Differential Revision: D20671293

fbshipit-source-id: e84d6a67d6c1ec28fb5e52d4607db8e92561a4cd
2020-03-30 11:27:41 -07:00
Jeremy Reizenstein
27eb791e2f fix recent lint
Summary: Flowing of compositing comments

Reviewed By: nikhilaravi

Differential Revision: D20556707

fbshipit-source-id: 4abdc85e4f65abd41c4a890b6895bc5e95b4576b
2020-03-30 06:17:27 -07:00
Patrick Labatut
d57daa6f85 Address black + isort fbsource linter warnings
Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
2020-03-29 14:51:02 -07:00
Jeremy Reizenstein
37c5c8e0b6 Linter, deprecated type()
Summary: Run linter after recent changes. Fix long comment in knn.h which clang-format has reflowed badly. Add crude test that code doesn't call deprecated `.type()` or `.data()`.

Reviewed By: nikhilaravi

Differential Revision: D20692935

fbshipit-source-id: 28ce0308adae79a870cb41a810b7cf8744f41ab8
2020-03-29 14:02:58 -07:00
Patrick Labatut
3061c5b663 Fix saving / loading empty OBJ files
Summary:
OBJ files without vertices or faces should be allowed:
- an OBJ with only vertices can represent a point cloud
- an OBJ without any vertex or face is just empty
- an OBJ with faces referencing inexistent vertices has invalid data

Reviewed By: gkioxari

Differential Revision: D20392526

fbshipit-source-id: e72c846ff1e5787fb11d527af3fefa261f9eb0ee
2020-03-28 08:14:00 -07:00
Justin Johnson
870290df34 Implement K-Nearest Neighbors
Summary:
Implements K-Nearest Neighbors with C++ and CUDA versions.

KNN in CUDA is highly nontrivial. I've implemented a few different versions of the kernel, and we heuristically dispatch to different kernels based on the problem size. Some of the kernels rely on template specialization on either D or K, so we use template metaprogramming to compile specialized versions for ranges of D and K.

These kernels are up to 3x faster than our existing 1-nearest-neighbor kernels, so we should also consider swapping out `nn_points_idx` to use these kernels in the backend.

I've been working mostly on the CUDA kernels, and haven't converged on the correct Python API.

I still want to benchmark against FAISS to see how far away we are from their performance.

Reviewed By: bottler

Differential Revision: D19729286

fbshipit-source-id: 608ffbb7030c21fe4008f330522f4890f0c3c21a
2020-03-26 13:40:26 -07:00
Jeremy Reizenstein
595aca27ea use assertClose
Summary: use assertClose in some tests, which enforces shape equality. Fixes some small problems, including graph_conv on an empty graph.

Reviewed By: nikhilaravi

Differential Revision: D20556912

fbshipit-source-id: 60a61eafe3c03ce0f6c9c1a842685708fb10ac5b
2020-03-23 11:36:38 -07:00
Georgia Gkioxari
03f441e7ca run lint
Summary: Run `/dev/linter.sh` to fix linting

Reviewed By: nikhilaravi

Differential Revision: D20584037

fbshipit-source-id: 69e45b33d22e3d54b6d37c3c35580bb3e9dc50a5
2020-03-21 17:58:15 -07:00
Georgia Gkioxari
6c48ff6ad9 replace view with reshape, check for nans
Summary: Replace view with reshape, add check for nans before mesh sampling

Reviewed By: nikhilaravi

Differential Revision: D20548456

fbshipit-source-id: c4e1b88e033ecb8f0f3a8f3a33a04ce13a5b5043
2020-03-19 19:31:41 -07:00
Olivia
53599770dd Accumulate points (#4)
Summary:
Code for accumulating points in the z-buffer in three ways:
1. weighted sum
2. normalised weighted sum
3. alpha compositing

Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/4

Reviewed By: nikhilaravi

Differential Revision: D20522422

Pulled By: gkioxari

fbshipit-source-id: 5023baa05f15e338f3821ef08f5552c2dcbfc06c
2020-03-19 11:23:12 -07:00
Nikhila Ravi
3d3b2fdc46 Re-sync with internal repository 2020-03-18 10:35:27 -07:00
Dave Greenwood
2480723adf create extrinsic from eye point (#65)
Summary:
Create extrinsic parameters from eye point.
Create the rotation and translation from an eye point, look-at point and up vector.
see:
https://www.khronos.org/registry/OpenGL-Refpages/gl2.1/xhtml/gluLookAt.xml

It is arguably easier to initialise a camera position as a point in the world rather than an angle.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/65

Reviewed By: bottler

Differential Revision: D20419652

Pulled By: nikhilaravi

fbshipit-source-id: 9caa1330860bb8bde1fb5c3864ed4cde836a5d19
2020-03-17 17:04:19 -07:00
Patrick Labatut
c9742d00b0 Enable spelling linter for Markdown, reStructuredText and IPython notebooks
Summary: Enable spelling linter for Markdown, reStructuredText and IPython notebooks under `fbcode/vision/fair`. Apply suggested fixes.

Reviewed By: ppwwyyxx

Differential Revision: D20495298

fbshipit-source-id: 95310c7b51f9fa68ba2aa34ecc39a874da36a75c
2020-03-17 16:33:20 -07:00
Nikhila Ravi
5d3cc3569a Rendering texturing fixes
Summary:
Fix errors raised by issue on GitHub - extending mesh textures + rendering with Gourad and Phong shaders.

https://github.com/facebookresearch/pytorch3d/issues/97

Reviewed By: gkioxari

Differential Revision: D20319610

fbshipit-source-id: d1c692ff0b9397a77a9b829c5c731790de70c09f
2020-03-17 08:58:40 -07:00
Jeremy Reizenstein
fa81953380 test_build
Summary: Ensure copyright header consistency and translation unit name uniqueness.

Reviewed By: nikhilaravi

Differential Revision: D20438802

fbshipit-source-id: 9820cfe4c6efab016a0a8589dfa24bb526692f83
2020-03-16 07:54:56 -07:00
Nikhila Ravi
20e457ca0e [pytorch3d[ padded to packed function in struct utils
Summary: Added a padded to packed utils function which takes either split sizes or a padding value to remove padded elements from a tensor.

Reviewed By: gkioxari

Differential Revision: D20454238

fbshipit-source-id: 180b807ff44c74c4ee9d5c1ac3b5c4a9b4be57c7
2020-03-15 09:35:58 -07:00
Patrick Labatut
d91c1d365b Add more complex mesh I/O benchmarks
Summary: Add more complex mesh I/O benchmarks: simple yet non-trivial procedural donut mesh

Reviewed By: nikhilaravi

Differential Revision: D20390726

fbshipit-source-id: b28b7e3a7f1720823c6bd24faabf688bb0127b7d
2020-03-13 04:31:25 -07:00
Patrick Labatut
098554d323 Use more realistic number of vertices / faces in benchmarks
Summary: Use more realistic number of vertices / faces in benchmarks: in typical meshes, |F| ~ 2 |V| (follows from Euler formula + triangles as faces)

Reviewed By: nikhilaravi

Differential Revision: D20390722

fbshipit-source-id: d615e5810d6f4521391963b2573497c08a58db80
2020-03-12 10:39:45 -07:00
Patrick Labatut
94fc862ff7 Simplify mesh I/O benchmarking methods
Summary: Rename mesh I/O benchmarking methods: always (re-)create file-like object and directly return a lambda

Reviewed By: nikhilaravi

Differential Revision: D20390723

fbshipit-source-id: b45236360869cccdf3d5458a0aafb3ebe269babe
2020-03-12 10:39:45 -07:00
Patrick Labatut
797e468e45 Rename mesh I/O benchmarks and associated methods
Summary:
Rename mesh I/O benchmarks and associated methods:
- add `simple` qualifier (benchmark on more realistic mesh data to be added later)
- align naming between OBJ and PLY
- prefix with `bm_` to make the benchmarking purpose clear(er)

Reviewed By: nikhilaravi

Differential Revision: D20390764

fbshipit-source-id: 7714520abfcfe1125067f3c52f7ce19bca359574
2020-03-12 10:39:45 -07:00
Patrick Labatut
3c71ab64cc Remove shebang line when not strictly required
Summary: The shebang line `#!<path to interpreter>` is only required for Python scripts, so remove it on source files for class or function definitions. Additionally explicitly mark as executable the actual Python scripts in the codebase.

Reviewed By: nikhilaravi

Differential Revision: D20095778

fbshipit-source-id: d312599fba485e978a243292f88a180d71e1b55a
2020-03-12 10:39:44 -07:00
Jeremy Reizenstein
fb97ab104e getitem for textures
Summary: Make Meshes.__getitem__ carry texture information to the new mesh.

Reviewed By: gkioxari

Differential Revision: D20283976

fbshipit-source-id: d9ee0580c11ac5b4384df9d8158a07e6eb8d00fe
2020-03-11 07:45:44 -07:00
Nikhila Ravi
15c72be444 Fix coordinate system conventions in renderer
Summary:
## Updates

- Defined the world and camera coordinates according to this figure. The world coordinates are defined as having +Y up, +X left and +Z in.

{F230888499}

- Removed all flipping from blending functions.
- Updated the rasterizer to return images with +Y up and +X left.
- Updated all the mesh rasterizer tests
    - The expected values are now defined in terms of the default +Y up, +X left
    - Added tests where the triangles in the meshes are non symmetrical so that it is clear which direction +X and +Y are

## Questions:
- Should we have **scene settings** instead of raster settings?
    - To be more correct we should be [z clipping in the rasterizer based on the far/near clipping planes](https://github.com/ShichenLiu/SoftRas/blob/master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu#L400) - these values are also required in the blending functions so should we make these scene level parameters and have a scene settings tuple which is available to the rasterizer and shader?

Reviewed By: gkioxari

Differential Revision: D20208604

fbshipit-source-id: 55787301b1bffa0afa9618f0a0886cc681da51f3
2020-03-06 06:51:05 -08:00
Georgia Gkioxari
767d68a3af pointcloud structure
Summary:
Revisions to Poincloud data structure with added normals

The biggest changes form the previous version include:
a) If the user provides tensor inputs, we make no assumption about padding. Padding is only for internal use for us to convert from list to padded
b) If features are not provided or if the poincloud is empty, all forms of features are None. This is so that we don't waste memory on holding dummy tensors.

Reviewed By: nikhilaravi

Differential Revision: D19791851

fbshipit-source-id: 7e182f7bb14395cb966531653f6dd6b328fd999c
2020-03-04 13:16:43 -08:00
Nikhila Ravi
ba11c0b59c Blending fixes and test updates
Summary:
Changed `torch.cumprod` to `torch.prod` in blending functions and added more tests and benchmark tests.

This should fix the issue raised on GitHub.

Reviewed By: gkioxari

Differential Revision: D20163073

fbshipit-source-id: 4569fd37be11aa4435a3ce8736b55622c00ec718
2020-02-29 17:52:05 -08:00
Nikhila Ravi
ff19c642cb Barycentric clipping in the renderer and flat shading
Summary:
Updates to the Renderer to enable barycentric clipping. This is important when there is blurring in the rasterization step.

Also added support for flat shading.

Reviewed By: jcjohnson

Differential Revision: D19934259

fbshipit-source-id: 036e48636cd80d28a04405d7a29fcc71a2982904
2020-02-28 21:30:33 -08:00
Dave Greenwood
a0f3dc2d63 allow changes to background_color in BlendParams (#64)
Summary:
BlendParams background_color is immutable , type hint as a sequence allows setting new values in constructor.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/64

Reviewed By: bottler

Differential Revision: D20068911

Pulled By: nikhilaravi

fbshipit-source-id: c580a7654dca25629218513841aa16d9d1055588
2020-02-24 13:44:02 -08:00
Jeremy Reizenstein
e491efb81f lint things
Summary:
Lint related fixes: Improve internal/OSS consistency. Fix the fight between black and certain pyre-ignore markers by moving them to the line before.
Use clang-format-8 automatically if present. Small number of pyre fixes.

arc doesn't run pyre at the moment, so I put back the explicit call to pyre. I don't know if there's an option somewhere to change this.

Reviewed By: nikhilaravi

Differential Revision: D19780518

fbshipit-source-id: ef1c243392322fa074130f6cff2dd8a6f7738a7f
2020-02-21 05:05:06 -08:00
Georgia Gkioxari
a3baa367e3 face areas backward
Summary:
Added backward for mesh face areas & normals. Exposed it as a layer. Replaced the computation with the new op in Meshes and in Sample Points.

Current issue: Circular imports. I moved the import of the op in meshes inside the function scope.

Reviewed By: jcjohnson

Differential Revision: D19920082

fbshipit-source-id: d213226d5e1d19a0c8452f4d32771d07e8b91c0a
2020-02-20 11:11:33 -08:00
Patrick Labatut
9ca5489107 Fix spelling of "Gouraud"
Summary: Fix spelling of *Gouraud* in [Gouraud shading](https://en.wikipedia.org/wiki/Gouraud_shading).

Reviewed By: nikhilaravi

Differential Revision: D19943547

fbshipit-source-id: 5c016b7b051a7b33a7b68ed5303b642d9e834bbd
2020-02-20 01:11:56 -08:00
Nikhila Ravi
f0dc65110a Shader API more consistent naming
Summary:
Renamed shaders to be prefixed with Hard/Soft depending on if they use a probabalistic blending (Soft) or use the closest face (Hard).

There is some code duplication but I thought it would be cleaner to have separate shaders for each task rather than:
- inheritance (which we discussed previously that we want to avoid)
- boolean (hard/soft) or a string (hard/soft) - new blending functions other than the ones provided would need if statements in the current shaders which might get messy.

Also added a `flat_shading` function and a `FlatShader` - I could make this into a tutorial as it was really easy to add a new shader and it might be a nice showcase.

NOTE: There are a few more places where the naming will need to change (e.g the tutorials) but I wanted to reach a consensus on this before changing it everywhere.

Reviewed By: jcjohnson

Differential Revision: D19761036

fbshipit-source-id: f972f6530c7f66dc5550b0284c191abc4a7f6fc4
2020-02-19 23:16:50 -08:00
Georgia Gkioxari
60f3c4e7d2 cpp support for packed to padded
Summary:
Cpu implementation for packed to padded and added gradients
```
Benchmark                                     Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
PACKED_TO_PADDED_2_100_300_1_cpu                    138             221           3625
PACKED_TO_PADDED_2_100_300_1_cuda:0                 184             261           2716
PACKED_TO_PADDED_2_100_300_16_cpu                   555             726            901
PACKED_TO_PADDED_2_100_300_16_cuda:0                179             260           2794
PACKED_TO_PADDED_2_100_3000_1_cpu                   396             519           1262
PACKED_TO_PADDED_2_100_3000_1_cuda:0                181             274           2764
PACKED_TO_PADDED_2_100_3000_16_cpu                 4517            5003            111
PACKED_TO_PADDED_2_100_3000_16_cuda:0               224             397           2235
PACKED_TO_PADDED_2_1000_300_1_cpu                   138             212           3616
PACKED_TO_PADDED_2_1000_300_1_cuda:0                180             282           2775
PACKED_TO_PADDED_2_1000_300_16_cpu                  565             711            885
PACKED_TO_PADDED_2_1000_300_16_cuda:0               179             264           2797
PACKED_TO_PADDED_2_1000_3000_1_cpu                  389             494           1287
PACKED_TO_PADDED_2_1000_3000_1_cuda:0               180             271           2777
PACKED_TO_PADDED_2_1000_3000_16_cpu                4522            5170            111
PACKED_TO_PADDED_2_1000_3000_16_cuda:0              216             286           2313
PACKED_TO_PADDED_10_100_300_1_cpu                   251             345           1995
PACKED_TO_PADDED_10_100_300_1_cuda:0                178             262           2806
PACKED_TO_PADDED_10_100_300_16_cpu                 2354            2750            213
PACKED_TO_PADDED_10_100_300_16_cuda:0               178             291           2814
PACKED_TO_PADDED_10_100_3000_1_cpu                 1519            1786            330
PACKED_TO_PADDED_10_100_3000_1_cuda:0               179             237           2791
PACKED_TO_PADDED_10_100_3000_16_cpu               24705           25879             21
PACKED_TO_PADDED_10_100_3000_16_cuda:0              228             316           2191
PACKED_TO_PADDED_10_1000_300_1_cpu                  261             432           1919
PACKED_TO_PADDED_10_1000_300_1_cuda:0               181             261           2756
PACKED_TO_PADDED_10_1000_300_16_cpu                2349            2770            213
PACKED_TO_PADDED_10_1000_300_16_cuda:0              180             256           2782
PACKED_TO_PADDED_10_1000_3000_1_cpu                1613            1929            310
PACKED_TO_PADDED_10_1000_3000_1_cuda:0              183             253           2739
PACKED_TO_PADDED_10_1000_3000_16_cpu              22041           23653             23
PACKED_TO_PADDED_10_1000_3000_16_cuda:0             220             343           2270
PACKED_TO_PADDED_32_100_300_1_cpu                   555             750            901
PACKED_TO_PADDED_32_100_300_1_cuda:0                188             282           2661
PACKED_TO_PADDED_32_100_300_16_cpu                 7550            8131             67
PACKED_TO_PADDED_32_100_300_16_cuda:0               181             272           2770
PACKED_TO_PADDED_32_100_3000_1_cpu                 4574            6327            110
PACKED_TO_PADDED_32_100_3000_1_cuda:0               173             254           2884
PACKED_TO_PADDED_32_100_3000_16_cpu               70366           72563              8
PACKED_TO_PADDED_32_100_3000_16_cuda:0              349             654           1433
PACKED_TO_PADDED_32_1000_300_1_cpu                  612             728            818
PACKED_TO_PADDED_32_1000_300_1_cuda:0               189             295           2647
PACKED_TO_PADDED_32_1000_300_16_cpu                7699            8254             65
PACKED_TO_PADDED_32_1000_300_16_cuda:0              189             311           2646
PACKED_TO_PADDED_32_1000_3000_1_cpu                5105            5261             98
PACKED_TO_PADDED_32_1000_3000_1_cuda:0              191             260           2625
PACKED_TO_PADDED_32_1000_3000_16_cpu              87073           92708              6
PACKED_TO_PADDED_32_1000_3000_16_cuda:0             344             425           1455
--------------------------------------------------------------------------------

Benchmark                                           Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
PACKED_TO_PADDED_TORCH_2_100_300_1_cpu                    492             627           1016
PACKED_TO_PADDED_TORCH_2_100_300_1_cuda:0                 768             975            652
PACKED_TO_PADDED_TORCH_2_100_300_16_cpu                   659             804            760
PACKED_TO_PADDED_TORCH_2_100_300_16_cuda:0                781             918            641
PACKED_TO_PADDED_TORCH_2_100_3000_1_cpu                   624             734            802
PACKED_TO_PADDED_TORCH_2_100_3000_1_cuda:0                778             929            643
PACKED_TO_PADDED_TORCH_2_100_3000_16_cpu                 2609            2850            192
PACKED_TO_PADDED_TORCH_2_100_3000_16_cuda:0               758             901            660
PACKED_TO_PADDED_TORCH_2_1000_300_1_cpu                   467             612           1072
PACKED_TO_PADDED_TORCH_2_1000_300_1_cuda:0                772             905            648
PACKED_TO_PADDED_TORCH_2_1000_300_16_cpu                  689             839            726
PACKED_TO_PADDED_TORCH_2_1000_300_16_cuda:0               789            1143            635
PACKED_TO_PADDED_TORCH_2_1000_3000_1_cpu                  629             735            795
PACKED_TO_PADDED_TORCH_2_1000_3000_1_cuda:0               812             916            616
PACKED_TO_PADDED_TORCH_2_1000_3000_16_cpu                2716            3117            185
PACKED_TO_PADDED_TORCH_2_1000_3000_16_cuda:0              844            1288            593
PACKED_TO_PADDED_TORCH_10_100_300_1_cpu                  2387            2557            210
PACKED_TO_PADDED_TORCH_10_100_300_1_cuda:0               4112            4993            122
PACKED_TO_PADDED_TORCH_10_100_300_16_cpu                 3385            4254            148
PACKED_TO_PADDED_TORCH_10_100_300_16_cuda:0              3959            4902            127
PACKED_TO_PADDED_TORCH_10_100_3000_1_cpu                 2918            3105            172
PACKED_TO_PADDED_TORCH_10_100_3000_1_cuda:0              4054            4450            124
PACKED_TO_PADDED_TORCH_10_100_3000_16_cpu               12748           13623             40
PACKED_TO_PADDED_TORCH_10_100_3000_16_cuda:0             4023            4395            125
PACKED_TO_PADDED_TORCH_10_1000_300_1_cpu                 2258            2492            222
PACKED_TO_PADDED_TORCH_10_1000_300_1_cuda:0              3997            4312            126
PACKED_TO_PADDED_TORCH_10_1000_300_16_cpu                3404            3597            147
PACKED_TO_PADDED_TORCH_10_1000_300_16_cuda:0             3877            4227            129
PACKED_TO_PADDED_TORCH_10_1000_3000_1_cpu                2789            3054            180
PACKED_TO_PADDED_TORCH_10_1000_3000_1_cuda:0             3821            4402            131
PACKED_TO_PADDED_TORCH_10_1000_3000_16_cpu              11967           12963             42
PACKED_TO_PADDED_TORCH_10_1000_3000_16_cuda:0            3729            4290            135
PACKED_TO_PADDED_TORCH_32_100_300_1_cpu                  6933            8152             73
PACKED_TO_PADDED_TORCH_32_100_300_1_cuda:0              11856           12287             43
PACKED_TO_PADDED_TORCH_32_100_300_16_cpu                 9895           11205             51
PACKED_TO_PADDED_TORCH_32_100_300_16_cuda:0             12354           13596             41
PACKED_TO_PADDED_TORCH_32_100_3000_1_cpu                 9516           10128             53
PACKED_TO_PADDED_TORCH_32_100_3000_1_cuda:0             12917           13597             39
PACKED_TO_PADDED_TORCH_32_100_3000_16_cpu               41209           43783             13
PACKED_TO_PADDED_TORCH_32_100_3000_16_cuda:0            12210           13288             41
PACKED_TO_PADDED_TORCH_32_1000_300_1_cpu                 7179            7689             70
PACKED_TO_PADDED_TORCH_32_1000_300_1_cuda:0             11896           12381             43
PACKED_TO_PADDED_TORCH_32_1000_300_16_cpu               10127           15494             50
PACKED_TO_PADDED_TORCH_32_1000_300_16_cuda:0            12034           12817             42
PACKED_TO_PADDED_TORCH_32_1000_3000_1_cpu                8743           10251             58
PACKED_TO_PADDED_TORCH_32_1000_3000_1_cuda:0            12023           12908             42
PACKED_TO_PADDED_TORCH_32_1000_3000_16_cpu              39071           41777             13
PACKED_TO_PADDED_TORCH_32_1000_3000_16_cuda:0           11999           13690             42
--------------------------------------------------------------------------------
```

Reviewed By: bottler, nikhilaravi, jcjohnson

Differential Revision: D19870575

fbshipit-source-id: 23a2477b73373c411899633386c87ab034c3702a
2020-02-19 10:48:54 -08:00