Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation

Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1042-1051


We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses. While many previous works learn to hallucinate the shape directly from priors, we resort to further improving the shape quality by leveraging cross-view information with a graph convolutional network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively. Inspired by traditional multiple view geometry methods, our network samples nearby area around the initial mesh's vertex locations and reasons an optimal deformation using perceptual feature statistics built from multiple input images. Extensive experiments show that our model produces accurate 3D shape that are not only visually plausible from the input perspectives, but also well aligned to arbitrary viewpoints. With the help of physically driven architecture, our model also exhibits generalization capability across different semantic categories, number of input images, and quality of mesh initialization.

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[pdf] [supp]
author = {Wen, Chao and Zhang, Yinda and Li, Zhuwen and Fu, Yanwei},
title = {Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}