Reconstructing continuous surfaces from 3D point clouds is a fundamental
operation in 3D geometry processing. Several recent state-of-the-art methods
address this problem using neural networks to learn signed distance functions
(SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that
is simple and leads to high quality SDFs. Specifically, we train a neural
network to pull query 3D locations to their closest points on the surface using
the predicted signed distance values and the gradient at the query locations,
both of which are computed by the network itself. The pulling operation moves
each query location with a stride given by the distance predicted by the
network. Based on the sign of the distance, this may move the query location
along or against the direction of the gradient of the SDF. This is a
differentiable operation that allows us to update the signed distance value and
the gradient simultaneously during training. Our outperforming results under
widely used benchmarks demonstrate that we can learn SDFs more accurately and
flexibly for surface reconstruction and single image reconstruction than the
state-of-the-art methods.

本文介绍了一种新的方法 Neural-Pull，利用神经网络简单且高质量地学习表面的符号距离函数，通过网络本身计算的符号距离值和查询位置的梯度来训练神经网络。在广泛使用的基准测试下，我们的结果优于现有最先进的方法，展示了对表面重建和单张图像重建可以更准确和灵活地学习符号距离函数。

神经拉力：学习从点云中学习符号距离函数并学习将空间拉到表面上

Neural-Pull: Learning Signed Distance Functions from Point Clouds by  Learning to Pull Space onto Surfaces

Spatially localized deformation components are very useful for shape analysis
and synthesis in 3D geometry processing. Several methods have recently been
developed, with an aim to extract intuitive and interpretable deformation
components. However, these techniques suffer from fundamental limitations
especially for meshes with noise or large-scale deformations, and may not
always be able to identify important deformation components. In this paper we
propose a novel mesh-based autoencoder architecture that is able to cope with
meshes with irregular topology. We introduce sparse regularization in this
framework, which along with convolutional operations, helps localize
deformations. Our framework is capable of extracting localized deformation
components from mesh data sets with large-scale deformations and is robust to
noise. It also provides a nonlinear approach to reconstruction of meshes using
the extracted basis, which is more effective than the current linear
combination approach. Extensive experiments show that our method outperforms
state-of-the-art methods in both qualitative and quantitative evaluations.

本文提出了一种基于网格自编码器的架构，利用稀疏正则化与卷积运算在处理嘈杂数据和大规模形变的网格时提取局部形变成分，此框架提供了一种以非线性的方式进行基础网格重建的方法，实验表明该方法优于目前的线性组合方法