Neural Kernel Surface Reconstruction
This addresses the challenge of efficient and robust 3D surface reconstruction for applications in computer vision and graphics, though it is incremental as it builds upon Neural Kernel Fields.
The paper tackles the problem of reconstructing 3D implicit surfaces from large-scale, sparse, and noisy point clouds, achieving state-of-the-art results on benchmarks and enabling reconstruction of millions of points in seconds.
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.