Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
This addresses a key challenge in 3D computer vision for applications like robotics and graphics, though it is an incremental improvement over existing methods.
The paper tackles the problem of normal estimation for unstructured 3D point clouds by proposing Nesti-Net, which uses a multi-scale representation and a mixture-of-experts CNN architecture, achieving state-of-the-art results on a synthetic benchmark.
In this paper, we propose a normal estimation method for unstructured 3D point clouds. This method, called Nesti-Net, builds on a new local point cloud representation which consists of multi-scale point statistics (MuPS), estimated on a local coarse Gaussian grid. This representation is a suitable input to a CNN architecture. The normals are estimated using a mixture-of-experts (MoE) architecture, which relies on a data-driven approach for selecting the optimal scale around each point and encourages sub-network specialization. Interesting insights into the network's resource distribution are provided. The scale prediction significantly improves robustness to different noise levels, point density variations and different levels of detail. We achieve state-of-the-art results on a benchmark synthetic dataset and present qualitative results on real scanned scenes.