- 25 May, 2022 1 commit
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Jeremy Reizenstein authored
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
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- 04 Jan, 2022 1 commit
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Jeremy Reizenstein authored
Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
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- 22 Jun, 2021 1 commit
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Patrick Labatut authored
Summary: License lint codebase Reviewed By: theschnitz Differential Revision: D29001799 fbshipit-source-id: 5c59869911785b0181b1663bbf430bc8b7fb2909
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- 16 Jul, 2020 1 commit
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Roman Shapovalov authored
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
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- 09 Jul, 2020 1 commit
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David Novotny authored
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
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- 15 May, 2020 1 commit
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Roman Shapovalov authored
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
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- 17 Apr, 2020 1 commit
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Roman Shapovalov authored
Summary: Efficient PnP algorithm to fit 2D to 3D correspondences under perspective assumption. Benchmarked both variants of nullspace and pick one; SVD takes 7 times longer in the 100K points case. Reviewed By: davnov134, gkioxari Differential Revision: D20095754 fbshipit-source-id: 2b4519729630e6373820880272f674829eaed073
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