A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation

2026-08-27

AI Summary

本文提出一种基于几何驱动的数据优化方法，通过主轴对齐解决物体姿态估计中的噪声敏感、对称性混淆问题，且无需修改现有网络架构。

Abstract

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 occlusions; Symmetry-Aware Canonicalization-explicitly resolving rotational ambiguities for symmetric objects at the data level, which fundamentally eliminates label confusion during network training; and Framework Agnosticism-the optimization is applied purely at the dataset level, ensuring plug-and-play compatibility with existing networks without any architectural modification. We validate the framework across diverse category-level and instance-level models. Extensive experiments demonstrate consistent and significant accuracy improvements, while preserving the integrity of the baseline network. This work establishes a new, geometry-driven direction for enhancing pose estimation, circumventing the need for complex network redesign.

Problem

• object pose estimation
• data-centric optimization
• rotational ambiguities
• symmetric objects
• robust representation

Innovation

• principal axes alignment
• inherent stability
• symmetry-aware canonicalization
• framework-agnostic