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

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12346)

Abstract

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.

Keywords

Normal estimation Surface fitting Least squares Unstructured 3D point clouds 3D point cloud deep learning 

Notes

Acknowledgments

This research was conducted by the Australian Research Council Centre of Excellence for Robotic Vision (CE140100016).

Supplementary material

500725_1_En_2_MOESM1_ESM.zip (31.8 mb)
Supplementary material 1 (zip 32581 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Australian Centre for Robotic VisionAustralian National UniversityCanberraAustralia

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