Multi-view 3D Reconstruction with Transformer

• URL: http://arxiv.org/abs/2103.12957v1
• Date: Wed, 24 Mar 2021 03:14:49 GMT
• Title: Multi-view 3D Reconstruction with Transformer
• Authors: Dan Wang, Xinrui Cui, Xun Chen, Zhengxia Zou, Tianyang Shi, Septimiu Salcudean, Z. Jane Wang, Rabab Ward
• Abstract summary: We reformulate the multi-view 3D reconstruction as a sequence-to-sequence prediction problem. We propose a new framework named 3D Volume Transformer (VolT) for such a task. Our method achieves a new state-of-the-art accuracy in multi-view reconstruction with fewer parameters.
• Score: 34.756336770583154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these methods - multi-view feature extraction and fusion, are usually investigated separately, and the object relations in different views are rarely explored. In this paper, inspired by the recent great success in self-attention-based Transformer models, we reformulate the multi-view 3D reconstruction as a sequence-to-sequence prediction problem and propose a new framework named 3D Volume Transformer (VolT) for such a task. Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network. A natural advantage of our design lies in the exploration of view-to-view relationships using self-attention among multiple unordered inputs. On ShapeNet - a large-scale 3D reconstruction benchmark dataset, our method achieves a new state-of-the-art accuracy in multi-view reconstruction with fewer parameters ($70\%$ less) than other CNN-based methods. Experimental results also suggest the strong scaling capability of our method. Our code will be made publicly available.

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