ShapeFormer: Transformer-Based Shape Completion via Sparse Representation
Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Daniel Cohen-Or, Hui Huang
Abstract
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant distribution can then be sampled to generate likely completions, each of which exhibits plausible shape details, while being faithful to the input. To facilitate the use of transformers for 3D, we introduce a compact 3D representation, vector quantized deep implicit function (VQDIF), that utilizes spatial sparsity to represent a close approximation of a 3D shape by a short sequence of discrete variables. Experiments demonstrate that ShapeFormer outperforms prior art for shape completion from ambiguous partial inputs in terms of both completion quality and diversity. We also show that our approach effectively handles a variety of shape types, incomplete patterns, and real-world scans.
BibTeX
@inproceedings{cvpr2022_shapeformertrans,
title = {ShapeFormer: Transformer-Based Shape Completion via Sparse Representation},
author = {Xingguang Yan and Liqiang Lin and Niloy J. Mitra and Dani Lischinski and Daniel Cohen-Or and Hui Huang},
booktitle = {CVPR 2022},
year = {2022}
}