Auto-ORVNet: Orientation-Boosted Volumetric Neural Architecture Search for 3D Shape Classification

Recently, more and more 3D shape datasets have become publicly available and significant results have been attained in 3D shape classification with 3D volumetric convolutional neural networks. However, the existing 3D volumetric networks have a problem with balancing model scale and classification accuracy. To address this problem, neural architecture search (NAS) was introduced into 3D shape classification tasks to search for a model satisfying both requirements. Automatically generating neural networks under NAS has attracted increasing research interest in recent years. The models learned by NAS outperform many manually designed networks in several 2D tasks like image classification, detection and semantic segmentation. In this paper, the differentiable formulation of NAS is exploited to search for several repeatable computation cells. The introduction of many light-weight designs for 3D CNNs assists in the construction of deep models with fewer parameters. The loss for the classification task along with the loss for orientation prediction are combined to guide such search. Extensive experiments are designed to evaluate candidate models on three datasets. The results demonstrate that without any pretraining, our discovered model for 3D shape classification outperforms most manually designed networks with small parameter sizes, whilst also showing that our model achieves a balance between model scale and classification accuracy.