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

4DRecons: 4D Neural Implicit Deformable Objects Reconstruction from a  single RGB-D Camera with Geometrical and Topological Regularizations

The process of using one image to guide the filtering process of another one
is called Guided Image Filtering (GIF). The main challenge of GIF is the
structure inconsistency between the guidance image and the target image.
Besides, noise in the target image is also a challenging issue especially when
it is heavy. In this paper, we propose a general framework for Robust Guided
Image Filtering (RGIF), which contains a data term and a smoothness term, to
solve the two issues mentioned above. The data term makes our model
simultaneously denoise the target image and perform GIF which is robust against
the heavy noise. The smoothness term is able to make use of the property of
both the guidance image and the target image which is robust against the
structure inconsistency. While the resulting model is highly non-convex, it can
be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in
an efficient manner. For challenging applications such as guided depth map
upsampling, we further develop a data-driven parameter optimization scheme to
properly determine the parameter in our model. This optimization scheme can
help to preserve small structures and sharp depth edges even for a large
upsampling factor (8x for example). Moreover, the specially designed structure
of the data term and the smoothness term makes our model perform well in
edge-preserving smoothing for single-image tasks (i.e., the guidance image is
the target image itself). This paper is an extension of our previous work [1],
[2].

本文提出了一种基于数据项和光滑项的 Robust Guided Image Filtering（RGIF）通用框架，旨在解决引导图像与目标图像之间的结构不一致性以及目标图像中噪声的问题。该模型可同时去噪目标图像和执行对噪声具有鲁棒性的 GIF 操作，并具有良好的边缘保持平滑处理效果。通过提出的迭代加权最小二乘法（IRLS）方法来解决高度非凸问题，进一步开发基于数据驱动的参数优化方案，以适当确定模型中参数。