  This paper presents ViewFormer, a simple yet effective model for multi-view
3d shape recognition and retrieval. We systematically investigate the existing
methods for aggregating multi-view information and propose a novel ``view set"
perspective, which minimizes the relation assumption about the views and
releases the representation flexibility. We devise an adaptive attention model
to capture pairwise and higher-order correlations of the elements in the view
set. The learned multi-view correlations are aggregated into an expressive view
set descriptor for recognition and retrieval. Experiments show the proposed
method unleashes surprising capabilities across different tasks and datasets.
For instance, with only 2 attention blocks and 4.8M learnable parameters,
ViewFormer reaches 98.8% recognition accuracy on ModelNet40 for the first time,
exceeding previous best method by 1.1% . On the challenging RGBD dataset, our
method achieves 98.4% recognition accuracy, which is a 4.1% absolute
improvement over the strongest baseline. ViewFormer also sets new records in
several evaluation dimensions of 3D shape retrieval defined on the SHREC'17
benchmark.
