We propose a new method to reconstruct the 3D human body from RGB-D images
with occlusions. The foremost challenge is the incompleteness of the RGB-D data
due to occlusions between the body and the environment, leading to implausible
reconstructions that suffer from severe human-scene penetration. To reconstruct
a semantically and physically plausible human body, we propose to reduce the
solution space based on scene information and prior knowledge. Our key idea is
to constrain the solution space of the human body by considering the occluded
body parts and visible body parts separately: modeling all plausible poses
where the occluded body parts do not penetrate the scene, and constraining the
visible body parts using depth data. Specifically, the first component is
realized by a neural network that estimates the candidate region named the
"free zone", a region carved out of the open space within which it is safe to
search for poses of the invisible body parts without concern for penetration.
The second component constrains the visible body parts using the "truncated
shadow volume" of the scanned body point cloud. Furthermore, we propose to use
a volume matching strategy, which yields better performance than surface
matching, to match the human body with the confined region. We conducted
experiments on the PROX dataset, and the results demonstrate that our method
produces more accurate and plausible results compared with other methods.

通过对场景信息和先前知识进行建模，我们提出了一种新的方法来从 RGB-D 图像中重建带有遮挡的 3D 人体，通过建模可能的姿势空间并使用深度数据约束可见身体部分，我们的方法在 PROX 数据集上的实验结果表明，与其他方法相比，能够产生更准确和合理的结果。