  Multi-view projection methods have demonstrated their ability to reach
state-of-the-art performance on 3D shape recognition. Those methods learn
different ways to aggregate information from multiple views. However, the
camera view-points for those views tend to be heuristically set and fixed for
all shapes. To circumvent the lack of dynamism of current multi-view methods,
we propose to learn those view-points. In particular, we introduce the
Multi-View Transformation Network (MVTN) that regresses optimal view-points for
3D shape recognition, building upon advances in differentiable rendering. As a
result, MVTN can be trained end-to-end along with any multi-view network for 3D
shape classification. We integrate MVTN in a novel adaptive multi-view pipeline
that can render either 3D meshes or point clouds. MVTN exhibits clear
performance gains in the tasks of 3D shape classification and 3D shape
retrieval without the need for extra training supervision. In these tasks, MVTN
achieves state-of-the-art performance on ModelNet40, ShapeNet Core55, and the
most recent and realistic ScanObjectNN dataset (up to 6% improvement).
Interestingly, we also show that MVTN can provide network robustness against
rotation and occlusion in the 3D domain. The code is available at
https://github.com/ajhamdi/MVTN .
