Deep Surface Reconstruction from Point Clouds with Visibility Information

TL;DR

This paper introduces methods to incorporate visibility information into point cloud data, significantly enhancing neural surface reconstruction accuracy and generalization to new shapes.

Contribution

It proposes simple augmentation techniques for point clouds with visibility data, improving surface reconstruction performance with minimal network modifications.

Findings

Enhanced reconstruction accuracy

Improved generalization to unseen shapes

Visibility augmentation benefits

Abstract

Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In this paper, we present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains. Our code and data is available at https://github.com/raphaelsulzer/dsrv-data.