无需类别标签，统一模型实现9自由度物体位姿估计。
UniPose9D: Universal Category-Agnostic Object Pose Estimation
- 基于点对匹配与DINOv2+PointNet特征，直接预测物体三维位姿。
- 在6个数据集上超越专用模型，支持未见物体和真实场景泛化。
- 解决对称性歧义，适配工业检测与机器人抓取等实际应用。
物体位姿估计是3D视觉的核心问题。尽管现有先进方法表现优异，但常过拟合于特定基准，泛化能力有限。本文提出UniPose9D，一种面向9自由度物体位姿估计的类别无关基础模型：仅需实例掩码或感兴趣区域（ROI），以及RGB-D图像或带预测深度的RGB图像，即可估计旋转、平移和尺度，无需类别标签、CAD模型、均值形状先验或参考视角。具体地，模型从观测几何中采样点对，利用DINOv2和PointNet特征预测每对点的NOCS坐标。为提升精度，引入基于点对的RANSAC N跳Kabsch-Umeyama算法，采用自适应阈值。进一步通过流匹配处理对称性歧义，并整合多个公开数据集的标注构建大规模训练集。在六个数据集上的实验表明，单一统一模型可达到甚至超过专用方法性能，且能泛化至未见物体和真实场景。代码与模型已开源。
原文摘要 · Abstract (English)
Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, they often overfit to existing benchmarks and exhibit limited generalization to novel categories and unseen scenes. We propose UniPose9D, a category-agnostic foundation model for 9D object pose estimation: given an instance mask/ROI and either an RGB-D observation or an RGB image with predicted depth, the model estimates rotation, translation, and metric size without category labels, CAD models, mean-shape priors, or reference views. Specifically, UniPose9D samples point pairs from the observed object geometry and uses DINOv2 and PointNet features to predict NOCS coordinates for each pair. To improve accuracy, we introduce a point-pair-based RANSAC N-hop Kabsch--Umeyama algorithm with an adaptive threshold. We further employ flow matching to address symmetric ambiguities and construct a large-scale training set by curating and aligning pose annotations from existing public datasets. Experiments across six datasets show that a single unified model can match or surpass specialist methods while generalizing to unseen objects and in-the-wild scenarios. Our code and model are available on https://github.com/qq456cvb/UniPose9D.
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