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.

翻译：我们提出一种新方法，可从存在遮挡的RGB-D图像中重建三维人体姿态。首要挑战在于人体与环境间的遮挡导致RGB-D数据不完整，进而产生受严重人体-场景穿透问题影响的不可靠重建。为重建语义与物理双重合理的人体形态，我们提出基于场景信息与先验知识缩减解空间的方案。核心思想在于将遮挡与可见身体部位的解空间分别约束：建模所有被遮挡部位不穿透场景的合理姿态，同时利用深度数据约束可见部位。具体而言，第一项实现通过神经网络估算名为"自由区域"的候选空间——该区域是从开阔空间中分割出的安全搜索空间，可在其中搜索不可见身体部位姿态而无需担心穿透问题；第二项通过扫描人体点云的"截断阴影体"约束可见部位。此外，我们提出采用体积匹配策略（较表面匹配效果更优）将人体与限定区域进行匹配。在PROX数据集上的实验表明，相较其他方法，本方法能生成更精确且合理的三维人体重建结果。