6 Commits

Author SHA1 Message Date
Roman Shapovalov
cdaac5f9c5 Bumping the threshold to allow leeway for CI testing randomness.
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
2020-07-16 10:19:43 -07:00
David Novotny
daf9eac801 Efficient PnP weighting bug fix
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
2020-07-09 06:40:38 -07:00
Roman Shapovalov
a8377f1f06 Numerical stability of ePnP.
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
2020-05-15 01:36:21 -07:00
Roman Shapovalov
54b482bd66 Not normalising control points by X.std()
Summary:
davnov134 found that the algorithm crashes if X is an axis-aligned plane. This is because I implemented scaling control points by `X.std()` as a poor man’s version of PCA whitening.
I checked that it does not bring consistent improvements, so let’s get rid of it.

The algorithm still results in slightly higher errors on the axis aligned planes but at least it does not crash. As a next step, I will experiment with detecting a planar case and using 3-point barycentric coordinates rather than 4-points.

Reviewed By: davnov134

Differential Revision: D21179968

fbshipit-source-id: 1f002fce5541934486b51808be0e910324977222
2020-04-23 06:04:54 -07:00
Jeremy Reizenstein
6207c359b1 spelling and flake
Summary: mostly recent lintish things

Reviewed By: nikhilaravi

Differential Revision: D21089003

fbshipit-source-id: 028733c1d875268f1879e4481da475b7100ba0b6
2020-04-17 10:50:22 -07:00
Roman Shapovalov
04d8bf6a43 Efficient PnP.
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
2020-04-17 07:44:16 -07:00