AI 中文总结
该研究提出一种几何驱动、框架无关的物体姿态估计数据优化方法，通过主轴线对齐构建旋转表示，提升了姿态估计精度且无需修改网络架构。
AI 中文摘要
当前物体姿态估计研究仍以模型为核心，聚焦于架构创新与后处理优化。本文提出一种以数据为核心的优化方法，通过主轴线对齐构建了一种新的、基于物理原理的旋转表示。该方法将物体坐标系与其固有几何轴线对齐，几何轴线由惯性属性推导而来，具有三大核心优势：固有稳定性——利用主轴线的能量最小化特性，提供对噪声和遮挡不敏感的鲁棒表示；对称感知规范化——在数据层面显式解决对称物体的旋转歧义，从根本上消除网络训练中的标签混淆；框架无关性——该优化仅在数据集层面应用，确保与现有网络即插即用兼容，无需任何架构修改。我们在不同类别级和实例级模型上验证了该框架，大量实验表明其在保持基线网络完整性的同时，实现了持续且显著的精度提升。本研究为提升姿态估计确立了新的几何驱动方向，规避了复杂网络重新设计的需求。
英文摘要
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.
CommentsSubmitted to TPAMI, under review
