SOURCE-LINKED INTELLIGENCE
A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation
Current object pose estimation research remains predominantly model-centric, focusing on architectural innovations and post-processing refinements. This paper introduces a data-centric optimization by proposing a novel, physically grounded rotation representation through principal axes alignment. Our method aligns the object's coordinate system with its inherent geometric axes, derived from inertial properties, yielding three key advantages: Inherent Stability-leveraging the energy-minimizing property of principal axes provides a robust representation that is less sensitive to noise and occlus
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T09:26:23.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.