Dynamic scene understanding is an essential capability in robotics and VR/AR.
In this paper we propose Co-Section, an optimization-based approach to 3D
dynamic scene reconstruction, which infers hidden shape information from
intersection constraints. An object-level dynamic SLAM frontend detects,
segments, tracks and maps dynamic objects in the scene. Our optimization
backend completes the shapes using hull and intersection constraints between
the objects. In experiments, we demonstrate our approach on real and synthetic
dynamic scene datasets. We also assess the shape completion performance of our
method quantitatively. To the best of our knowledge, our approach is the first
method to incorporate such physical plausibility constraints on object
intersections for shape completion of dynamic objects in an energy minimization
framework.

本文提出了 Co-Section 算法以推断出隐藏的场景形状信息并在动态场景中进行建模，同时在进行物理可行性约束建模的能量最小化框架中完成动态对象的形状补全。实验结果表明，本方法在真实和合成的动态场景数据集上表现突出。