发表机构
Stanford University; Amazon（斯坦福大学; 亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对物体姿态估计中现有方法泛化性不足的问题，提出UniPose9D模型，通过采样点对、利用特定特征预测NOCS坐标，引入改进算法及流匹配，构建训练集，实验证明该模型能匹配或超越专业方法，泛化能力强。
AI 中文摘要
物体姿态估计是3D视觉中的一个基本问题。尽管最近的先进方法取得了不错的性能，但它们往往过度拟合现有基准，对新类别和未见场景的泛化能力有限。我们提出了UniPose9D，一种用于9D物体姿态估计的类别无关基础模型。给定实例掩码/感兴趣区域以及RGB-D观测或带有预测深度的RGB图像，该模型无需类别标签、CAD模型、平均形状先验或参考视图就能估计旋转、平移和度量尺寸。具体来说，UniPose9D从观测到的物体几何中采样点对，并使用DINOv2和PointNet特征预测每对点的NOCS坐标。为提高准确性，引入了基于点对的RANSAC N跳Kabsch-Umeyama算法及自适应阈值。还采用流匹配解决对称模糊性，并通过整理和对齐现有公共数据集的姿态注释构建大规模训练集。六个数据集的实验表明，单个统一模型能匹配或超越专业方法，同时泛化到未见物体和野外场景。
英文摘要
Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalization to novel categories and unseen scenes remains challenging. We propose UniPose9D, a unified model for category-agnostic 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 eight datasets show that a single unified model achieves competitive performance on standard benchmarks while generalizing to unseen objects, unseen categories, and in-the-wild scenarios. Our code and model are available at https://github.com/qq456cvb/UniPose9D.
