GitLab

Summary: fixes https://github.com/facebookresearch/pytorch3d/issues/1679

Reviewed By: MichaelRamamonjisoa

Differential Revision: D50949933

fbshipit-source-id: 5c467de8bf84dd2a3d61748b3846678582d24ea3

Summary:
Torch CUDA extension for Marching Cubes
- MC involving 3 steps:
  - 1st forward pass to collect vertices and occupied state for each voxel
  - Compute compactVoxelArray to skip non-empty voxels
  - 2nd pass to genereate interpolated vertex positions and faces by marching through the grid
- In contrast to existing MC:
   - Bind each interpolated vertex with a global edge_id to address floating-point precision
   - Added deduplication process to remove redundant vertices and faces

Benchmarks (ms):

| N / V(^3)      | python          | C++             |   CUDA   | Speedup |
| 2 / 20          |    12176873  |       24338     |     4363   | 2790x/5x|
| 1 / 100          |     -             |    3070511     |   27126   |    113x    |
| 2 / 100          |     -             |    5968934     |   53129   |    112x    |
| 1 / 256          |     -             |  61278092     | 430900   |    142x    |
| 2 / 256          |     -             |125687930     | 856941   |    146x   |

Reviewed By: kjchalup

Differential Revision: D39644248

fbshipit-source-id: d679c0c79d67b98b235d12296f383d760a00042a

Summary:
Torch C++ extension for Marching Cubes

- Add torch C++ extension for marching cubes. Observe a speed up of ~255x-324x speed up (over varying batch sizes and spatial resolutions)

- Add C++ impl in existing unit-tests.

(Note: this ignores all push blocking failures!)

Reviewed By: kjchalup

Differential Revision: D39590638

fbshipit-source-id: e44d2852a24c2c398e5ea9db20f0dfaa1817e457

Summary: Overhaul of marching_cubes_naive for better performance and to avoid relying on unstable hashing. In particular, instead of hashing vertex positions, we index each interpolated vertex with its corresponding edge in the 3d grid.

Reviewed By: kjchalup

Differential Revision: D39419642

fbshipit-source-id: b5fede3525c545d1d374198928dfb216262f0ec0

Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS.

Reviewed By: shapovalov

Differential Revision: D36186940

fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b

Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: bottler

Differential Revision: D35553814

fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1

Summary: Update all FB license strings to the new format.

Reviewed By: patricklabatut

Differential Revision: D33403538

fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c

Summary: License lint codebase

Reviewed By: theschnitz

Differential Revision: D29001799

fbshipit-source-id: 5c59869911785b0181b1663bbf430bc8b7fb2909

Summary: Simplify finding the data directories in the tests.

Reviewed By: nikhilaravi

Differential Revision: D27634293

fbshipit-source-id: dc308a7c86c41e6fae56a2ab58187c9f0335b575

Summary: Make common functions for finding directories where test data is found, instead of lots of tests using their own `__file__`  while trying to get ./tests/data and the tutorials data.

Reviewed By: nikhilaravi

Differential Revision: D27633701

fbshipit-source-id: 1467bb6018cea16eba3cab097d713116d51071e9

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