  In 3D shape recognition, multi-view based methods leverage human's
perspective to analyze 3D shapes and have achieved significant outcomes. Most
existing research works in deep learning adopt handcrafted networks as
backbones due to their high capacity of feature extraction, and also benefit
from ImageNet pretraining. However, whether these network architectures are
suitable for 3D analysis or not remains unclear. In this paper, we propose a
neural architecture search method named Auto-MVCNN which is particularly
designed for optimizing architecture in multi-view 3D shape recognition.
Auto-MVCNN extends gradient-based frameworks to process multi-view images, by
automatically searching the fusion cell to explore intrinsic correlation among
view features. Moreover, we develop an end-to-end scheme to enhance retrieval
performance through the trade-off parameter search. Extensive experimental
results show that the searched architectures significantly outperform manually
designed counterparts in various aspects, and our method achieves
state-of-the-art performance at the same time.
