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.

本研究为多视角三维形状识别和检索提出了 ViewFormer 方法，该方法通过提出 “视角集” 视角，采用一种自适应的注意力模型来捕捉视图集中的元素之间的成对和高阶相关性，并将学习到的多视角相关性聚合到一个富有表达力的视角集描述符中进行识别和检索。实验证明，该方法在不同任务和数据集上具有惊人的能力。特别是在 ModelNet40 数据集上，ViewFormer 首次达到 98.8％的识别准确率，超过了之前最好的方法 1.1％的表现。