[ICRA21] Reconstructing Interactive 3D Scene by Panoptic Mapping and CAD Model Alignments

Abstract

In this paper, we rethink the problem of scene reconstruction from an embodied agent’s perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide actionable information for simulating interactions with agents. Here, we address this challenging problem by reconstructing an interactive scene using RGB-D data stream, which captures (i) the semantics and geometry of objects and layouts by a 3D volumetric panoptic mapping module, and (ii) object affordance and contextual relations by reasoning over physical common sense among objects, organized by a graph-based scene representation. Crucially, this reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models for finer-grained robot interactions. In the experiments, we demonstrate that (i) our panoptic mapping module outperforms previous state-of-the-art methods, (ii) a high-performant physical reasoning procedure that matches, aligns, and replaces objects’ meshes with best-fitted CAD models, and (iii) reconstructed scenes are physically plausible and naturally afford actionable interactions; without any manual labeling, they are seamlessly imported to ROS-based simulators and virtual environments for complex robot task executions.

Publication
In Proceedings of the IEEE International Conference on Robotics and Automation
Muzhi Han
Muzhi Han
Ph.D. Candidate
Zeyu Zhang
Zeyu Zhang
Ph.D. Candidate
Ziyuan Jiao
Ziyuan Jiao
Ph.D. Candidate
Xu Xie
Xu Xie
Research Scientist
Yixin Zhu
Yixin Zhu
Assistant Professor

I build humanlike AI.

Song-Chun Zhu
Song-Chun Zhu
Chair Professor
Hangxin Liu
Hangxin Liu
Research Scientist

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