外观
论文arXiv 2610.2108613 天前
RoPE-Flow：通过2D图条件3D流匹配进行基于单目视觉的机器人位姿估计
原文链接：RoPE-Flow: Monocular Vision-based Robot Pose Estimation via 2D Graph-Conditioned 3D Flow Matching（arXiv 2610.21086 · 发布日期：2026-10-15）
RoPE-Flow: Monocular Vision-based Robot Pose Estimation via 2D Graph-Conditioned 3D Flow Matching
RoPE-Flow用2D热图特征、图条件生成与3D流匹配估计单目相机到机器人位姿，并在多机器人多相机及跨场景基准验证。
核心要点
- 单目相机到机器人位姿估计需由2D观测恢复3D结构，现有方法存在迭代耗时与投影歧义等问题。
- 方法从2D热图提取关键点中心多尺度特征，并用可学习邻接图生成流匹配条件。
- 引入骨长损失、射线深度损失和关节加权，以增强拓扑一致性、尺度稳定并缓解关节难度差异。
- 实验显示其在不同类型机器人与多相机设置上优于现有方法，并建立跨场景零样本基准。
验证范围与局限：验证范围包括不同类型机器人、多相机位姿估计和跨场景零样本基准，摘要未说明是否为真机或仿真。
英文摘要 (Abstract)
Camera-to-robot pose estimation from a monocular image is significant for vision-based robot navigation and control. However, recovering 3D structural features from 2D observations remains a challenging task. Existing camera-to-robot pose estimation methods either rely on time-consuming iterative rendering or involve sophisticated 2D-3D registration which suffers from projection ambiguity, accumulated errors for distal joints and imbalanced learning for different joints. In this work, we propose RoPE-Flow, an end-to-end flow matching-based framework for camera-to-robot pose estimation. From the predicted 2D heatmaps, we propose a Keypoint-centered Feature Extractor to obtain positional features along with contextual representations from keypoints-centered multi-scale patches. Moreover, we introduce Graph-guided Condition Generator, which embeds a learnable adjacency map to generate condition for flow matching based on correlation among keypoints. Besides, we introduce Bone Length Loss and Ray-Depth Loss for topological consistency and scale stability, with joint-wise weighting to mitigate varied prediction difficulty across joints. The proposed method outperforms state-of-the-art methods for pose estimation across different types of robots with mutiple cameras. Besides, we establish a novel cross-scene benchmark to demonstrate the outstanding performance of RoPE-Flow on zero-shot cross-scene robot pose estimation. Meanwhile, animated visualizations are shown on the website: ropeflow.netlify.app
作者：Liangjing Shao, Wanhao Liu, Zhiwei Fang, Rulin Zhou, Quanlu Zhang, Changjing Liu, Beilei Cui, Yiming Huang, Hongliang Ren
来源
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