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arXiv Computer Vision By Liangjing Shao, Wanhao Liu, Zhiwei Fang, Rulin Zhou, Quanlu Zhang, Changjing Liu, Beilei Cui, Yiming Huang, Hongliang Ren

RoPE-Flow: Monocular Vision-based Robot Pose Estimation via 2D Graph-Conditioned 3D Flow Matching

Read the original on arXiv Computer Vision →

RoPE-Flow is an end‑to‑end framework that estimates camera‑to‑robot pose from a single monocular image using flow matching. It extracts keypoint‑centered features from predicted 2D heatmaps and employs a graph‑guided condition generator to encode keypoint correlations for flow matching. The method introduces Bone Length Loss and Ray‑Depth Loss to enforce topological consistency and scale stability, and demonstrates superior performance over state‑of‑the‑art approaches on multiple robot types and a new cross‑scene benchmark.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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