  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.
