Holistic 3D human-scene reconstruction is a crucial and emerging research
area in robot perception. A key challenge in holistic 3D human-scene
reconstruction is to generate a physically plausible 3D scene from a single
monocular RGB image. The existing research mainly proposes optimization-based
approaches for reconstructing the scene from a sequence of RGB frames with
explicitly defined physical laws and constraints between different scene
elements (humans and objects). However, it is hard to explicitly define and
model every physical law in every scenario. This paper proposes using an
implicit feature representation of the scene elements to distinguish a
physically plausible alignment of humans and objects from an implausible one.
We propose using a graph-based holistic representation with an encoded physical
representation of the scene to analyze the human-object and object-object
interactions within the scene. Using this graphical representation, we
adversarially train our model to learn the feasible alignments of the scene
elements from the training data itself without explicitly defining the laws and
constraints between them. Unlike the existing inference-time optimization-based
approaches, we use this adversarially trained model to produce a per-frame 3D
reconstruction of the scene that abides by the physical laws and constraints.
Our learning-based method achieves comparable 3D reconstruction quality to
existing optimization-based holistic human-scene reconstruction methods and
does not need inference time optimization. This makes it better suited when
compared to existing methods, for potential use in robotic applications, such
as robot navigation, etc.

通过使用隐式的场景元素特征表征来区分合理和不合理的人体和物体对齐，在机器人感知中提出了一个以图形为基础的整体三维人体场景重建的方法，该学习方法实现了与现有基于优化的方法相当的三维重建质量，且不需要推理时间优化，适用于机器人导航等潜在应用中。