论文标题
神经毛病：通过学习将空间拉到表面的学习签名距离功能
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
论文作者
论文摘要
从3D点云中重建连续表面是3D几何处理中的基本操作。使用神经网络来学习签名距离功能（SDFS）的一些最新最新方法解决了此问题。在本文中，我们介绍了\ textit {neural-pull}，这是一种简单的新方法，可导致高质量的SDF。具体而言，我们使用预测的签名距离值和查询位置的梯度训练神经网络将查询3D位置拉到表面上的最接点，这两者都是由网络本身计算的。拉动操作以网络预测的距离给出的大步移动每个查询位置。基于距离的符号，这可能会沿SDF梯度的方向移动查询位置。这是一个可区分的操作，使我们能够在培训期间同时更新签名的距离值和梯度。我们在广泛使用的基准下表现出色的结果表明，与最先进的方法相比，我们可以更准确，灵活地学习SDF进行表面重建和单图像重建。
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.
