This release brings PyTorch 1.10 builds and numerical fixes and improvements

Large fixes

• Raysampling now works with non-square image conventions commit
• Perspective_correct mesh rasterization calculation is protected against divide-by-zero. This fixes quite a number of bug reports, e.g. #561. commit

Breaking changes

• This commit makes camera code more consistent in behaving like align_corners=False everywhere, by removing some extra -1’s in the code for screen space conversion. (1) If you create a camera in screen coordinates, i.e. setting in_ndc=False, then anything you do with the camera which touches NDC space may be affected, including trying to use renderers. The transform_points_screen function will not be affected. (2) If you call the function “transform_points_screen” on a camera defined in NDC space then results will be different.

Bug fixes

• Raysampling now works with cameras defined in screen space. commit.
• Pointclouds.inside_box is now properly reduced rather than returning separate results per axis. commit
• PulsarPointsRenderer fixed with non-square cameras commit
• Functions clone and detach on TexturesUV properly propagate align_corners and padding_mode options. commit
• Fixing default arguments of add_points_features_to_volume_densities_features commit
• Fix opencv camera conversion for non-square images (affects pulsar) commit

Small improvements

• Some matrix conversions are now traceable with torch.jit. commit
• Fixes to compiled code to make windows builds happier commit commit
• A new set of tests, test_camera_pixels, illustrates the precise mapping of pixels for cameras. commit
• Numerical improvements in IOU calculation commit and commit
• New option on TexturesUV to choose sampling_mode. commit
• The plotly visualization will show face colors from a TexturesAtlas with K=1 commit

Internal

• Benchmarks have been moved to tests/benchmarks commit
• Spelling commit
• Some more type annotations for Pointclouds commit
• Test fixes commit
• Special implementation of eigenvalue calculation for 3x3 matrices which can be faster and more reliable than native PyTorch. Currently not used commit