Summary: This fixes a corner case for multi-radius handling for the pulsar backend. The additional dimensionality check ensures that the batched parsing for radiuses is only performed when appropriate.
Reviewed By: bottler
Differential Revision: D25387708
fbshipit-source-id: c486dcf327f812265b7ca8ca5ef5c6a31e6d4549
Summary:
Similar to non square image rasterization for meshes, apply the same updates to the pointcloud rasterizer.
Main API Change:
- PointRasterizationSettings now accepts a tuple/list of (H, W) for the image size.
Reviewed By: jcjohnson
Differential Revision: D25465206
fbshipit-source-id: 7370d83c431af1b972158cecae19d82364623380
Summary: Add `return self` to the `to` function for the renderer classes.
Reviewed By: bottler
Differential Revision: D25534487
fbshipit-source-id: e8dbd35524f0bd40e835439e93184b5a1f1532ca
Summary: This diff updates the documentation and tutorials with information about the new pulsar backend. For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder docs/examples.
Reviewed By: nikhilaravi
Differential Revision: D24498129
fbshipit-source-id: e312b0169a72b13590df6e4db36bfe6190d742f9
Summary:
This diff builds on top of the `pulsar integration` diff to provide a unified interface for the existing PyTorch3D point renderer and Pulsar. For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder docs/examples.
The unified interfaces are completely consistent. Switching the render backend is as easy as using `renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)` instead of `renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)` and adding the `gamma` parameter to the forward function. All PyTorch3D camera types are supported as far as possible; keyword arguments are properly forwarded to the camera. The `PerspectiveCamera` and `OrthographicCamera` require znear and zfar as additional parameters for the forward pass.
Reviewed By: nikhilaravi
Differential Revision: D21421443
fbshipit-source-id: 4aa0a83a419592d9a0bb5d62486a1cdea9d73ce6
Summary:
This diff integrates the pulsar renderer source code into PyTorch3D as an alternative backend for the PyTorch3D point renderer. This diff is the first of a series of three diffs to complete that migration and focuses on the packaging and integration of the source code.
For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder `docs/examples`.
Tasks addressed in the following diffs:
* Add the PyTorch3D interface,
* Add notebook examples and documentation (or adapt the existing ones to feature both interfaces).
Reviewed By: nikhilaravi
Differential Revision: D23947736
fbshipit-source-id: a5e77b53e6750334db22aefa89b4c079cda1b443
Summary: Support rendering different color backgrounds for pointclouds for both compositors
Reviewed By: nikhilaravi
Differential Revision: D23611043
fbshipit-source-id: ab029650d51349340372c5bd66700e6577d48851
Summary: Update the transform step in the pointcloud rasterizer to use the `update_padded` method on `Pointclouds`. There was an inefficient step using `offset_points` which went via the packed represntation (and required unecessary additional memory). I think this was before the `update_padded` method was added to `Pointclouds`.
Reviewed By: gkioxari
Differential Revision: D22329166
fbshipit-source-id: 76db8a19654fb2f7807635d4f1c1729debdf3320
Summary:
Fixes the default setting of `max_points_per_bin` in `rasterize_points.py`. For large batches with large size pointclouds this was a causing the rasterizer to be very slow.
Expanded the pointcloud rendering benchmarks to include larger size pointclouds and fixed cuda synchronization issue in benchmark.
Reviewed By: gkioxari
Differential Revision: D22301185
fbshipit-source-id: 5077c1ba2c43d73efc1c659f0ec75959ceddf893
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: 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: Fix to enable a mesh/point rasterizer to be initialized without having to specify the camera.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21362359
fbshipit-source-id: 4f84ea18ad9f179c7b7c2289ebf9422a2f5e26de
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
