We present a novel method for reconstructing a 3D implicit surface from a
large-scale, sparse, and noisy point cloud. Our approach builds upon the
recently introduced Neural Kernel Fields (NKF) representation. It enjoys
similar generalization capabilities to NKF, while simultaneously addressing its
main limitations: (a) We can scale to large scenes through compactly supported
kernel functions, which enable the use of memory-efficient sparse linear
solvers. (b) We are robust to noise, through a gradient fitting solve. (c) We
minimize training requirements, enabling us to learn from any dataset of dense
oriented points, and even mix training data consisting of objects and scenes at
different scales. Our method is capable of reconstructing millions of points in
a few seconds, and handling very large scenes in an out-of-core fashion. We
achieve state-of-the-art results on reconstruction benchmarks consisting of
single objects, indoor scenes, and outdoor scenes.

本文提出了一种从大规模、稀疏且带噪声的点云中重建三维隐式表面的新方法，采用神经内核场（NKF）表示法，通过紧凑支持的核函数扩展规模，利用内存高效的稀疏线性求解器进行计算，并采用梯度拟合求解方法来提高抗噪性能，同时还最小化了训练要求，能够处理不同尺度物体和场景混合训练数据的情况。该方法能够在几秒钟内重建数百万个点，并能够处理非常大的场景。该方法在由单个物体、室内和室外场景组成的重建基准测试中取得了最先进的结果。