Papers › DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

23 Mar 2020ECCV 2020 8arXiv:2003.10826archive 2025-07-28

Yizhak Ben-Shabat, Stephen Gould

We propose a surface fitting method for unstructured 3D point clouds. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares polynomial surface fitting. The learned weights act as a soft selection for the neighborhood of surface points thus avoiding the scale selection required of previous methods. To train the network we propose a novel surface consistency loss that improves point weight estimation. The method enables extracting normal vectors and other geometrical properties, such as principal curvatures, the latter were not presented as ground truth during training. We achieve state-of-the-art results on a benchmark normal and curvature estimation dataset, demonstrate robustness to noise, outliers and density variations, and show its application on noise removal.

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