Summary of 3d Equivariant Pose Regression Via Direct Wigner-d Harmonics Prediction, by Jongmin Lee et al.
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
by Jongmin Lee, Minsu Cho
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A crucial task in 3D vision applications is determining the 3D orientations of objects from single images. Existing methods learn spatial representations using Euler angles or quaternions but introduce discontinuities. To overcome this, we propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression, aligning with spherical CNNs. Our SO(3)-equivariant pose harmonics predictor ensures consistent pose estimation under arbitrary rotations. Trained with frequency-domain regression loss, our method achieves state-of-the-art results on ModelNet10-SO(3) and PASCAL3D+, improving accuracy, robustness, and data efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture of an object from different angles. How do we know which way it’s facing? This is called single-image pose estimation. Most methods use special numbers to represent the orientation of the object, but these numbers can be tricky. We came up with a new way to predict the orientation using a type of mathematical function that helps computers process information more efficiently. Our method does better than others on certain benchmarks and is more reliable. |
Keywords
» Artificial intelligence » Pose estimation » Regression
