Summary: Make eps=1e-4 by default for coplanar check and also enable it to be set by the user in call to `box3d_overlap`.
Reviewed By: gkioxari
Differential Revision: D31596836
fbshipit-source-id: b57fe603fd136cfa58fddf836922706d44fe894e
Summary: PyTorch 1.6.0 came out on 28 Jul 2020. Stop builds for 1.5.0 and 1.5.1. Also update the news section of the README for recent releases.
Reviewed By: nikhilaravi
Differential Revision: D31442830
fbshipit-source-id: 20bdd8a07090776d0461240e71c6536d874615f6
Summary: The epsilon value is important for determining whether vertices are inside/outside a plane.
Reviewed By: gkioxari
Differential Revision: D31485247
fbshipit-source-id: 5517575de7c02f1afa277d00e0190a81f44f5761
Summary: Increase some test tolerances so that they pass in more situations, and re-enable two tests.
Reviewed By: nikhilaravi
Differential Revision: D31379717
fbshipit-source-id: 06a25470cc7b6d71cd639d9fd7df500d4b84c079
Summary:
For non square image, the NDC space in pytorch3d is not square [-1, 1]. Instead, it is [-1, 1] for the smallest side, and [-u, u] for the largest side, where u > 1. This behavior is followed by the pytorch3d renderer.
See the function `get_ndc_to_screen_transform` for a example.
Without this fix, the rendering result is not correct using the converted pytorch3d-camera from a opencv-camera on non square images.
This fix also helps the `transform_points_screen` function delivers consistent results with opencv projection for the converted pytorch3d-camera.
Reviewed By: classner
Differential Revision: D31366775
fbshipit-source-id: 8858ae7b5cf5c0a4af5a2af40a1358b2fe4cf74b
Summary: Do the website building in a docker container to avoid worrying about dependencies.
Reviewed By: nikhilaravi
Differential Revision: D30223892
fbshipit-source-id: 77b7b4630188167316891381f6ca9e9fbe7f0a05
Summary: A small fix for the iou3d note
Reviewed By: bottler
Differential Revision: D31370686
fbshipit-source-id: 6c97302b5c78de52915f31be70f234179c4b246d
Summary: New function to randomly subsample Pointclouds to a maximum size.
Reviewed By: nikhilaravi
Differential Revision: D30936533
fbshipit-source-id: 789eb5004b6a233034ec1c500f20f2d507a303ff
Summary: Added CUDA implementation to match the new, still unused, C++ function for the core of points2vols.
Reviewed By: nikhilaravi
Differential Revision: D29548608
fbshipit-source-id: 16ebb61787fcb4c70461f9215a86ad5f97aecb4e
Summary: Single C++ function for the core of points2vols, not used anywhere yet. Added ability to control align_corners and the weight of each point, which may be useful later.
Reviewed By: nikhilaravi
Differential Revision: D29548607
fbshipit-source-id: a5cda7ec2c14836624e7dfe744c4bbb3f3d3dfe2
Summary:
A note for our new algorithm for IoU of oriented 3D boxes. It includes
* A description of the algorithm
* A comparison with Objectron
Reviewed By: nikhilaravi
Differential Revision: D31288066
fbshipit-source-id: 0ea8da887bc5810bf4a3e0848223dd3590df1538
Summary: C++ Implementation of algorithm to compute 3D bounding boxes for batches of bboxes of shape (N, 8, 3) and (M, 8, 3).
Reviewed By: gkioxari
Differential Revision: D30905190
fbshipit-source-id: 02e2cf025cd4fa3ff706ce5cf9b82c0fb5443f96
Summary:
I have implemented an exact solution for 3D IoU of oriented 3D boxes.
This file includes:
* box3d_overlap: which computes the exact IoU of box1 and box2
* box3d_overlap_sampling: which computes an approximate IoU of box1 and box2 by sampling points within the boxes
Note that both implementations currently do not support batching.
Our exact IoU implementation is based on the fact that the intersecting shape of the two 3D boxes will be formed by segments of the surface of the boxes. Our algorithm computes these segments by reasoning whether triangles of one box are within the second box and vice versa. We deal with intersecting triangles by clipping them.
Reviewed By: gkioxari
Differential Revision: D30667497
fbshipit-source-id: 2f747f410f90b7f854eeaf3036794bc3ac982917
Summary: Add conda builds for the newly released PyTorch version 1.9.1.
Reviewed By: patricklabatut
Differential Revision: D31140206
fbshipit-source-id: 697549a3ef0db8248f4f9b5c00cf1407296b5022
Summary:
Copy some descriptions of renderer parameters to more places so they are easier to find.
Also a couple of small corrections, and make RasterizationSettings a dataclass.
Reviewed By: nikhilaravi, patricklabatut
Differential Revision: D30899822
fbshipit-source-id: 805cf366acb7d51cb308fa574deff0657c199673
Summary: Attempt to fix#659, an observation that the rasterizer is nondeterministic, by resolving tied faces by picking those with lower index.
Reviewed By: nikhilaravi, patricklabatut
Differential Revision: D30699039
fbshipit-source-id: 39ed797eb7e9ce7370ae71259ad6b757f9449923
Summary: Unlike other cu files, sigmoid_alpha_blend uses torch/extension.h. Avoid for possible build speed win and because of a reported problem #843 on windows with CUDA 11.4.
Reviewed By: nikhilaravi
Differential Revision: D31054121
fbshipit-source-id: 53a1f985a1695a044dfd2ee1a5b0adabdf280595
Summary: Rename sample_farthest_point.cpp to not match its CUDA equivalent.
Reviewed By: nikhilaravi
Differential Revision: D31006645
fbshipit-source-id: 135b511cbde320d2b3e07fc5b027971ef9210aa9
Summary: Remove use of nonstandard C++. Noticed on windows in issue https://github.com/facebookresearch/pytorch3d/issues/843. (We use `__restrict__` in CUDA, where it is fine, even on windows)
Reviewed By: nikhilaravi
Differential Revision: D31006516
fbshipit-source-id: 929ba9b3216cb70fad3ffa3274c910618d83973f
Summary:
CUDA implementation of farthest point sampling algorithm.
## Visual comparison
Compared to random sampling, farthest point sampling gives better coverage of the shape.
{F658631262}
## Reduction
Parallelized block reduction to find the max value at each iteration happens as follows:
1. First split the points into two equal sized parts (e.g. for a list with 8 values):
`[20, 27, 6, 8 | 11, 10, 2, 33]`
2. Use half of the thread (4 threads) to compare pairs of elements from each half (e.g elements [0, 4], [1, 5] etc) and store the result in the first half of the list:
`[20, 27, 6, 33 | 11, 10, 2, 33]`
Now we no longer care about the second part but again divide the first part into two
`[20, 27 | 6, 33| -, -, -, -]`
Now we can use 2 threads to compare the 4 elements
4. Finally we have gotten down to a single pair
`[20 | 33 | -, - | -, -, -, -]`
Use 1 thread to compare the remaining two elements
5. The max will now be at thread id = 0
`[33 | - | -, - | -, -, -, -]`
The reduction will give the farthest point for the selected batch index at this iteration.
Reviewed By: bottler, jcjohnson
Differential Revision: D30401803
fbshipit-source-id: 525bd5ae27c4b13b501812cfe62306bb003827d2
Summary:
This is a naive python implementation of the iterative farthest point sampling algorithm along with associated simple tests. The C++/CUDA implementations will follow in subsequent diffs.
The algorithm is used to subsample a pointcloud with better coverage of the space of the pointcloud.
The function has not been added to `__init__.py`. I will add this after the full C++/CUDA implementations.
Reviewed By: jcjohnson
Differential Revision: D30285716
fbshipit-source-id: 33f4181041fc652776406bcfd67800a6f0c3dd58
Summary: Fix issue #826. This is a correction to the joining of TexturesUV into a single scene.
Reviewed By: nikhilaravi
Differential Revision: D30767092
fbshipit-source-id: 03ba6a1d2f22e569d1b3641cd13ddbb8dcb87ec7
Summary:
* HAT_INV_SKEW_SYMMETRIC_TOL was a global variable and torch script gives an error when compiling that function. Move it to the function scope.
* torch script gives error when compiling acos_linear_extrapolation because bound is a union of tuple and float. The tuple version is kept in this diff.
Reviewed By: patricklabatut
Differential Revision: D30614916
fbshipit-source-id: 34258d200dc6a09fbf8917cac84ba8a269c00aef
Summary: In D30349234 (1b8d86a104) we introduced persistent=False to some register_buffer calls, which depend on PyTorch 1.6. We go back to the old behaviour for PyTorch 1.5.
Reviewed By: nikhilaravi
Differential Revision: D30731327
fbshipit-source-id: ab02ef98ee87440ef02479b72f4872b562ab85b5
Summary:
There has historically been a lot of duplication between the coarse rasterization logic for point clouds and meshes. This diff factors out the shared logic, so coarse rasterization of point clouds and meshes share the same core logic.
Previously the only difference between the coarse rasterization kernels for points and meshes was the logic for checking whether a {point / triangle} intersects a tile in the image. We implement a generic coarse rasterization kernel that takes a set of 2D bounding boxes rather than geometric primitives; we then implement separate kernels that compute 2D bounding boxes for points and triangles.
This change does not affect the Python API at all. It also should not change any rasterization behavior, since this diff is just a refactoring of the existing logic.
I see this diff as the first in a few pieces of rasterizer refactoring. Followup diffs should do the following:
- Add a check for bin overflow in the generic coarse rasterizer kernel: allocate a global scalar to flag bin overflow which kernel worker threads can write to in case they detect bin overflow. The C++ launcher function can then check this flag after the kernel returns and issue a warning to the user in case of overflow.
- As a slightly more involved mechanism, if bin overflow is detected then the coarse kernel can continue running in order to count how many elements fall into each bin, without actually writing out their indices to the coarse output tensor. Then the actual number of entries per bin can be used to re-allocate the output tensor and re-run the coarse rasterization kernel so that bin overflow can be automatically avoided.
- The unification of the coarse and fine rasterization kernels also allows us to insert an extra CUDA kernel prior to coarse rasterization that filters out primitives outside the view frustum. This would be helpful for rendering full scenes (e.g. Matterport data) where only a small piece of the mesh is actually visible at any one time.
Reviewed By: bottler
Differential Revision: D25710361
fbshipit-source-id: 9c9dea512cb339c42adb3c92e7733fedd586ce1b
Summary: Renaming parts of the mesh coarse rasterization and separating the bounding box calculation. All in preparation for sharing code with point rasterization.
Reviewed By: bottler
Differential Revision: D30369112
fbshipit-source-id: 3508c0b1239b355030cfa4038d5f3d6a945ebbf4
Summary: In preparation for sharing coarse rasterization between point clouds and meshes, move the functions to a new file. No code changes.
Reviewed By: bottler
Differential Revision: D30367812
fbshipit-source-id: 9e73835a26c4ac91f5c9f61ff682bc8218e36c6a
Summary: Change cyclic deps test to be independent of test discovery order. Also let it work without plotly.
Reviewed By: nikhilaravi
Differential Revision: D30669614
fbshipit-source-id: 2eadf3f8b56b6096c5466ce53b4f8ac6df27b964
Summary: Regenerate config.yml after a recent bad merge which lost a few builds.
Reviewed By: nikhilaravi
Differential Revision: D30696918
fbshipit-source-id: 3ecdfca8682baed13692ec710aa7c25dbd24dd44
Summary: Fixes GitHub issue #751. The vectorized implementation of bilinear interpolation didn't properly handle the edge cases in the same way as the `grid_sample` method in PyTorch.
Reviewed By: bottler
Differential Revision: D30684208
fbshipit-source-id: edf241ecbd72d46b94ad340a4e601e26c83db88e
Summary: Replace master with main in hard coded paths or mentions in documentation
Reviewed By: bottler
Differential Revision: D30696097
fbshipit-source-id: d5ff67bb026d90d1543d10ab027f916e8361ca69
Summary:
As suggested in #802. By not persisting the _xy_grid buffer, we can allow (in some cases) a model with one image_size to be loaded from a saved model which was trained at a different resolution.
Also avoid persisting _frequencies in HarmonicEmbedding for similar reasons.
BC-break: This will cause load_state_dict, in strict mode, to complain if you try to load an old model with the new code.
Reviewed By: patricklabatut
Differential Revision: D30349234
fbshipit-source-id: d6061d1e51c9f79a78d61a9f732c9a5dfadbbb47
Summary:
Use PyTorch3D's new faster sample_pdf function instead of local Python implementation.
Also clarify deps for the Python implementation.
Reviewed By: gkioxari
Differential Revision: D30512109
fbshipit-source-id: 84cfdc00313fada37a6b29837de96f6a4646434f
Summary:
Great work! :)
Just found a link in the examples that is not working. This will fix it.
Best,
Alex
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/818
Reviewed By: nikhilaravi
Differential Revision: D30637532
Pulled By: patricklabatut
fbshipit-source-id: ed6c52375d1e760cb0fb2c0a66648dfeb0c6ed46
