From Point Clouds to Mesh Using Regression

Lubor Ladicky, Olivier Saurer, SoHyeon Jeong, Fabio Maninchedda, Marc Pollefeys; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3893-3902


Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps. Methods, performing only local operations in the vicinity of individual points, are very fast, but reconstructed models typically contain lots of visually unpleasant holes. On the other hand, regularized volumetric approaches, formulated as a global optimization, are typically too slow for real-time interactive applications. We propose to use a regression forest based method, which predicts the projection of a grid point to the surface, depending on the spatial configuration of point density in the grid point neighborhood. We designed a suitable feature vector and efficient oct-tree based GPU evaluation, capable of predicting surface of high resolution 3D models in milliseconds. Our method learns and predicts surfaces from an observed point cloud sparser than the evaluation grid, and therefore effectively acts as a regularizer.

Related Material

author = {Ladicky, Lubor and Saurer, Olivier and Jeong, SoHyeon and Maninchedda, Fabio and Pollefeys, Marc},
title = {From Point Clouds to Mesh Using Regression},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}