Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
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发表时间：
2020-11
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通讯作者：
Baorui Ma;Zhizhong Han;Yu-Shen Liu;Matthias Zwicker
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作者：
Baorui Ma;Zhizhong Han;Yu-Shen Liu;Matthias Zwicker
从三维点云数据重建连续曲面是三维几何处理中的一项基本操作。最近的几种最先进的方法使用神经网络来学习符号距离函数（SDF）来解决这个问题。在本文中，我们介绍了神经拉动，一种新的方法，是简单的，并导致高质量的SDF。具体来说，我们训练一个神经网络，使用预测的有符号距离值和查询位置的梯度将查询3D位置拉到表面上最近的邻居，这两者都是由网络本身计算的。拉取操作以由网络预测的距离给定的步幅移动每个查询位置。基于距离的符号，这可以沿着或逆着SDF的梯度的方向移动查询位置。这是一个可微操作，允许我们在训练过程中同时更新带符号距离值和梯度。我们在广泛使用的基准测试下的出色结果表明，与最先进的方法相比，我们可以更准确，更灵活地学习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 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 neighbors 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.