外观
论文arXiv 2610.1679316 天前
ViewStruct：视图锚定的关节结构与连接性推断
原文链接：ViewStruct: View-Grounded Articulation and Connectivity Inference（arXiv 2610.16793 · 发布日期：2026-10-12）
ViewStruct: View-Grounded Articulation and Connectivity Inference
ViewStruct基于视图锚定表示和适配的预训练视觉语言模型，从RGB、分割与深度联合推断连接和关节，并在真实视频与仿真操纵中验证。
核心要点
- 面向陌生物体交互，需要理解物体部件之间如何连接以及如何运动。
- 输入对齐的RGB图像、部件分割和深度，可结合图像或视频，预测刚体分组、附着和关节参数。
- 其表示将预测关联到观测区域，并在重力对齐、视图相对坐标中表达关节几何，无需类别规范朝向。
- 实验显示结构预测优于所评估基线，可迁移到未见类别、资产来源和真实世界人类交互视频。
验证范围与局限：任务特定训练仅使用合成数据，机器人运动轨迹用途通过仿真操纵实验验证。
英文摘要 (Abstract)
Interacting with unfamiliar objects requires understanding how their parts connect and move. We introduce ViewStruct, a framework for jointly predicting connectivity and articulation in objects and scenes. Given an RGB image with aligned part segmentation and depth, optionally supplemented by images or video, ViewStruct predicts rigid-body groupings, attachments, and joint parameters. Its view-grounded representation links predictions to observed regions and expresses joint geometry in a gravity-aligned, view-relative frame, without requiring category-specific canonical orientations. A pretrained vision--language model is adapted to predict structure in seconds, without requiring observed articulation motion or per-instance optimisation. Experiments demonstrate improved structural prediction over evaluated baselines and transfer to unseen categories, asset sources, and real-world human-interaction videos, despite task-specific training exclusively on synthetic data. Simulated manipulation experiments further demonstrate the utility of predicted joints for generating robot motion trajectories.
作者：Wenjing Bian, Ranran Huang, Andreas Geiger
来源
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