Scan2Mesh: From Unstructured Range Scans to 3D Meshes

Angela Dai, Matthias Niessner; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5574-5583

Abstract


We introduce Scan2Mesh, a novel data-driven generative approach which transforms an unstructured and potentially incomplete range scan into a structured 3D mesh representation. The main contribution of this work is a generative neural network architecture whose input is a range scan of a 3D object and whose output is an indexed face set conditioned on the input scan. In order to generate a 3D mesh as a set of vertices and face indices, the generative model builds on a series of proxy losses for vertices, edges, and faces. At each stage, we realize a one-to-one discrete mapping between the predicted and ground truth data points with a combination of convolutional- and graph neural network architectures. This enables our algorithm to predict a compact mesh representation similar to those created through manual artist effort using 3D modeling software. Our generated mesh results thus produce sharper, cleaner meshes with a fundamentally different structure from those generated through implicit functions, a first step in bridging the gap towards artist-created CAD models.

Related Material


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[bibtex]
@InProceedings{Dai_2019_CVPR,
author = {Dai, Angela and Niessner, Matthias},
title = {Scan2Mesh: From Unstructured Range Scans to 3D Meshes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}