Jongmin presented a paper at NeurIPS 2024 in Vancouver

The paper addresses one of the central challenges in 3D vision: accurately determining object orientations from a single image. It introduces a novel approach that leverages SO(3)-equivariant pose harmonics in the frequency domain, enabling consistent and robust pose estimation under arbitrary rotations. This method avoids the pitfalls of traditional spatial parameterizations, such as discontinuities in Euler angles and singularities in quaternions.

Through extensive evaluation, the model demonstrates state-of-the-art performance on key benchmarks like ModelNet10-SO(3) and PASCAL3D+, showing significant improvements in accuracy, robustness, and data efficiency.