This paper presents a novel approach 4DRecons that takes a single camera RGB-D sequence of a dynamic subject as input and outputs a complete textured deforming 3D model over time. 4DRecons encodes the output as a 4D neural implicit surface and presents an optimization procedure that combines a data term and two regularization terms. The data term fits the 4D implicit surface to the input partial observations. We address fundamental challenges in fitting a complete implicit surface to partial observations. The first regularization term enforces that the deformation among adjacent frames is as rigid as possible (ARAP). To this end, we introduce a novel approach to compute correspondences between adjacent textured implicit surfaces, which are used to define the ARAP regularization term. The second regularization term enforces that the topology of the underlying object remains fixed over time. This regularization is critical for avoiding self-intersections that are typical in implicit-based reconstructions. We have evaluated the performance of 4DRecons on a variety of datasets. Experimental results show that 4DRecons can handle large deformations and complex inter-part interactions and outperform state-of-the-art approaches considerably.

这篇论文介绍了一种名为4DRecons的新方法，它以动态物体的单个摄像机RGB-D序列作为输入，并输出随时间变化的完整纹理变形的3D模型。4DRecons将输出编码为4D神经隐式表面，并提出了一个结合数据项和两个正则化项的优化过程。实验证明，4DRecons能够处理大变形和复杂的部分相互作用，并且明显优于现有的方法。

4DRecons: 具有几何和拓扑正则化的单一RGB-D相机的4D神经隐含可变形物体重建