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：我们提出了一种新方法，用于从存在遮挡的RGB-D图像中重建三维人体模型。首要挑战在于人体与场景间的遮挡导致RGB-D数据不完整，进而产生不合理的人体重建结果，出现严重的人体-场景穿透现象。为重建语义与物理上合理的人体模型，我们提出基于场景信息与先验知识缩减解空间的策略。核心思想是通过分别处理被遮挡与可见的身体部位来约束人体解空间：对所有被遮挡部位不穿透场景的合理姿态进行建模，同时利用深度数据约束可见部位。具体而言，第一部分通过神经网络实现，该网络估计称为"自由区域"的候选空间——该区域从开放空间中分割而出，在此区域内搜索不可见部位姿态时无需顾虑穿透问题。第二部分利用扫描人体点云的"截断阴影体积"约束可见部位。此外，我们提出使用体积匹配策略（较表面匹配方法性能更优）将人体模型与受限区域进行匹配。我们在PROX数据集上开展实验，结果表明与现有方法相比，本方法能生成更精确、更合理的三维人体重建结果。