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: New test that each subpackage of pytorch3d imports cleanly.
Reviewed By: patricklabatut
Differential Revision: D30001632
fbshipit-source-id: ca8dcac94491fc22f33602b3bbef481cba927094
Summary: Small fix for omitting this argument.
Reviewed By: nikhilaravi
Differential Revision: D29548610
fbshipit-source-id: f25032fab3faa2f09006f5fcf8628138555f2f20
Summary: Fixes to a couple of comments on points to volumes, make the mask work in round_points_to_volumes, and remove a duplicate rand calculation
Reviewed By: nikhilaravi
Differential Revision: D29522845
fbshipit-source-id: 86770ba37ef3942b909baf63fd73eed1399635b6
Summary:
Implementation of ball query from PointNet++. This function is similar to KNN (find the neighbors in p2 for all points in p1). These are the key differences:
- It will return the **first** K neighbors within a specified radius as opposed to the **closest** K neighbors.
- As all the points in p2 do not need to be considered to find the closest K, the algorithm is much faster than KNN when p2 has a large number of points.
- The neighbors are not sorted
- Due to the radius threshold it is not guaranteed that there will be K neighbors even if there are more than K points in p2.
- The padding value for `idx` is -1 instead of 0.
# Note:
- Some of the code is very similar to KNN so it could be possible to modify the KNN forward kernels to support ball query.
- Some users might want to use kNN with ball query - for this we could provide a wrapper function around the current `knn_points` which enables applying the radius threshold afterwards as an alternative. This could be called `ball_query_knn`.
Reviewed By: jcjohnson
Differential Revision: D30261362
fbshipit-source-id: 66b6a7e0114beff7164daf7eba21546ff41ec450
Summary: Annotate the (return type of the) following dunder functions across the codebase: `__init__()`, `__len__()`, `__getitem__()`
Reviewed By: nikhilaravi
Differential Revision: D29001801
fbshipit-source-id: 928d9e1c417ffe01ab8c0445311287786e997c7c
Summary:
Refactor of all functions to compute laplacian matrices in one file.
Support for:
* Standard Laplacian
* Cotangent Laplacian
* Norm Laplacian
Reviewed By: nikhilaravi
Differential Revision: D29297466
fbshipit-source-id: b96b88915ce8ef0c2f5693ec9b179fd27b70abf9
Summary:
Get rid of pyre fixmes related to importing a native module:
- add stub file for the `_C` native extension to the internal typeshed
- add initial annotations to the new stub file
- remove the now unnecessary pyre ignores
Reviewed By: nikhilaravi
Differential Revision: D28929467
fbshipit-source-id: 6525e15c8f27215a3ff6f78392925fd0ed6ec2ac
Summary: Increase code coverage of subdivide_meshes and re-include it in code coverage test
Reviewed By: bottler
Differential Revision: D29097476
fbshipit-source-id: 3403ae38a90c4b53f24188eed11faae202a235b5
Summary: Omit specific code from code coverage computation. This is done to make code coverage test pass again. Test coverage for shader.py and subdivide_meshes.py will be increased in later diffs to re-include them.
Reviewed By: bottler
Differential Revision: D29061105
fbshipit-source-id: addac35a216c96de9f559e2d8fe42496adc85791
Summary: Make black and isort stop disagreeing by removing some unneeded comments around import statements. pyre ignores are moved.
Reviewed By: theschnitz
Differential Revision: D27118137
fbshipit-source-id: 9926d0f21142adcf9b5cfe1d394754317f6386df
Summary:
Defines a function to run marching cubes algorithm on a single or batch of 3D scalar fields. Returns a mesh's faces and vertices.
UPDATES (12/18)
- Input data is now specified as a (B, D, H, W) tensor as opposed to a (B, W, H, D) tensor. This will now be compatible with the Volumes datastructure.
- Add an option to return output vertices in local coordinates instead of world coordinates.
Also added a small fix to remove the dype for device in Transforms3D - if passing in a torch.device instead of str it causes a pyre error.
Reviewed By: jcjohnson
Differential Revision: D24599019
fbshipit-source-id: 90554a200319fed8736a12371cc349e7108aacd0
Summary:
There are a couple of options for supporting non square images:
1) NDC stays at [-1, 1] in both directions with the distance calculations all modified by (W/H). There are a lot of distance based calculations (e.g. triangle areas for barycentric coordinates etc) so this requires changes in many places.
2) NDC is scaled by (W/H) so the smallest side has [-1, 1]. In this case none of the distance calculations need to be updated and only the pixel to NDC calculation needs to be modified.
I decided to go with option 2 after trying option 1!
API Changes:
- Image size can now be specified optionally as a tuple
TODO:
- add a benchmark test for the non square case.
Reviewed By: jcjohnson
Differential Revision: D24404975
fbshipit-source-id: 545efb67c822d748ec35999b35762bce58db2cf4
Summary: Taubin Smoothing for filtering meshes and making them smoother. Taubin smoothing is an iterative approach.
Reviewed By: nikhilaravi
Differential Revision: D24751149
fbshipit-source-id: fb779e955f1a1f6750e704f1b4c6dfa37aebac1a
Summary: Move to a local import for calculating pointcloud normals, similar to _compute_face_areas_normals on Meshes.
Reviewed By: theschnitz
Differential Revision: D24695260
fbshipit-source-id: 9e1eb5d15017975b8c4f4175690cc3654f38d9a4
Summary:
As pointed out in #328, we had an indexing operation where a reshape would do and be faster. The resulting faces_packed_to_edges_packed is no longer contiguous.
Also fix a use of faces_packed_to_edges_packed which might modify the object unintentionally.
Reviewed By: theschnitz
Differential Revision: D24390292
fbshipit-source-id: 225677d8fcc1d6b76efad7706718ecdb5182ffe1
Summary:
Enhanced `sample_points_from_meshes` with texture sampling
* This new feature is used to return textures corresponding to the sampled points in `sample_points_from_meshes`
Reviewed By: nikhilaravi
Differential Revision: D24031525
fbshipit-source-id: 8e5d8f784cc38aa391aa8e84e54423bd9fad7ad1
Summary:
1. CircleCI tests fail because of different randomisation. I was able to reproduce it on devfair (with an older version of pytorch3d though), but with a new threshold, it works. Let’s push and see if it will work in CircleCI.
2. Fixing linter’s issue with `l` variable name.
Reviewed By: bottler
Differential Revision: D22573244
fbshipit-source-id: 32cebc8981883a3411ed971eb4a617469376964d
Summary: When rendering meshes with Phong shading, interpolate_face_attributes was taking up a nontrivial fraction of the overall shading time. This diff replaces our Python implementation of this function with a CUDA implementation.
Reviewed By: nikhilaravi
Differential Revision: D21610763
fbshipit-source-id: 2bb362a28f698541812aeab539047264b125ebb8
Summary:
There is a bug in efficient PnP that incorrectly weights points. This fixes it.
The test does not pass for the previous version with the bug.
Reviewed By: shapovalov
Differential Revision: D22449357
fbshipit-source-id: f5a22081e91d25681a6a783cce2f5c6be429ca6a
Summary:
Automatic run to suppress type errors.
#pyreupgrade
Differential Revision: D22369027
fbshipit-source-id: 2beb1a43e429a0850944a8849d416bedefd516ed
Summary: Added `sorted` argument to the `knn_points` function. This came up during the benchmarking against Faiss - sorting added extra memory usage. Match the memory usage of Faiss by making sorting optional.
Reviewed By: bottler, gkioxari
Differential Revision: D22329070
fbshipit-source-id: 0828ff9b48eefce99ce1f60089389f6885d03139
Summary: To avoid pytorch warnings and future behaviour changes, stop using torch.div and / with tensors of integers.
Reviewed By: gkioxari, mruberry
Differential Revision: D21857955
fbshipit-source-id: fb9f3000f3d953352cdc721d2a5f73d3a4bbf4b7
Summary:
This diff is auto-generated to upgrade the Pyre version and suppress errors in vision. The upgrade will affect Pyre local configurations in the following directories:
```
vision/ale/search
vision/fair/fvcore
vision/fair/pytorch3d
vision/ocr/rosetta_hash
vision/vogue/personalization
```
Differential Revision: D21688454
fbshipit-source-id: 1f3c3fee42b6da2e162fd0932742ab8c5c96aa45
Summary: lg-zhang found the problem with the quadratic part of ePnP implementation: n262385 . It was caused by a coefficient returned from the linear equation solver being equal to exactly 0.0, which caused `sign()` to return 0, something I had not anticipated. I also made sure we avoid division by zero by clamping all relevant denominators.
Reviewed By: nikhilaravi, lg-zhang
Differential Revision: D21531200
fbshipit-source-id: 9eb2fa9d4f4f8f5f411d4cf1cffcc44b365b7e51
Summary: pytorch is adding checks that mean integer tensors with requires_grad=True need to be avoided. Fix accidentally creating them.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21576712
fbshipit-source-id: 008218997986800a36d93caa1a032ee91f2bffcd
Summary:
davnov134 found that the algorithm crashes if X is an axis-aligned plane. This is because I implemented scaling control points by `X.std()` as a poor man’s version of PCA whitening.
I checked that it does not bring consistent improvements, so let’s get rid of it.
The algorithm still results in slightly higher errors on the axis aligned planes but at least it does not crash. As a next step, I will experiment with detecting a planar case and using 3-point barycentric coordinates rather than 4-points.
Reviewed By: davnov134
Differential Revision: D21179968
fbshipit-source-id: 1f002fce5541934486b51808be0e910324977222
