Summary:
Fix a bug which resulted in a rendering artifacts if the image size was not a multiple of 16.
Fix: Revert coarse rasterization to original implementation and only update fine rasterization to reverse the ordering of Y and X axis. This is much simpler than the previous approach!
Additional changes:
- updated mesh rendering end-end tests to check outputs from both naive and coarse to fine rasterization.
- added pointcloud rendering end-end tests
Reviewed By: gkioxari
Differential Revision: D21102725
fbshipit-source-id: 2e7e1b013dd6dd12b3a00b79eb8167deddb2e89a
Summary:
Applying the changes added for mesh rasterization to ensure that +Y is up and +X is left so that the coordinate system is right handed.
Also updated the diagram in the docs to indicate that (0,0) is in the top left hand corner.
Reviewed By: gkioxari
Differential Revision: D20394849
fbshipit-source-id: cfb7c79090eb1f55ad38b92327a74a70a8dc541e
Summary:
Update the point cloud rasterizer to:
- use the pointcloud datastructure (rebased on top of D19791851.)
- support rasterization of heterogeneous point clouds in the same way as with Meshes.
The main changes to the API will be as follows:
- The input to `rasterize_points` will be a `Pointclouds` object instead of a tensor. This will be easy to update e.g.
```
points = torch.randn(N, P, 3)
idx2, zbuf2, dists2 = rasterize_points(points, image_size, radius, points_per_pixel)
points = torch.randn(N, P, 3)
pointclouds = Pointclouds(points=points)
idx2, zbuf2, dists2 = rasterize_points(pointclouds, image_size, radius, points_per_pixel)
```
- The indices output from rasterization will now refer to points in `poinclouds.points_packed()`.
This may require some changes to the functions which consume the outputs of rasterization if they were previously
assuming that the indices ranged from 0 to P where P is the number of points in each pointcloud.
Making this change now so that Olivia can update her PR accordingly.
Reviewed By: gkioxari
Differential Revision: D20088651
fbshipit-source-id: 833ed659909712bcbbb6a50e2ec0189839f0413a
Summary:
Fixed a few MSVC compiler (visual studio 2019, MSVC 19.16.27034) compatibility issues
1. Replaced long with int64_t. aten::data_ptr\<long\> is not supported in MSVC
2. pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp, inline function is not correctly recognized by MSVC.
3. pytorch3d/csrc/rasterize_meshes/geometry_utils.cuh
const auto kEpsilon = 1e-30;
MSVC does not compile this const into both host and device, change to a MACRO.
4. pytorch3d/csrc/rasterize_meshes/geometry_utils.cuh,
const float area2 = pow(area, 2.0);
2.0 is considered as double by MSVC and raised an error
5. pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp
std::tuple<torch::Tensor, torch::Tensor> RasterizePointsCoarseCpu() return type does not match the declaration in rasterize_points_cpu.h.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/9
Reviewed By: nikhilaravi
Differential Revision: D19986567
Pulled By: yuanluxu
fbshipit-source-id: f4d98525d088c99c513b85193db6f0fc69c7f017
Summary:
Added if `WITH_CUDA` checks for points/mesh rasterization. If installing on cpu only then this causes `Undefined symbol` errors when trying to import pytorch3d.
We had these checks for all the other cuda files but not the rasterization files.
Thanks ppwwyyxx for the tip!
Reviewed By: ppwwyyxx, gkioxari
Differential Revision: D19801495
fbshipit-source-id: 20e7adccfdb33ac731c00a89414b2beaf0a35529
