Recognition of 3D Shapes Based on 3V-DepthPano CNN

Abstract

This paper proposes a convolutional neural network (CNN) with three branches based on the three-view drawing principle and depth panorama for 3D shape recognition. The three-view drawing principle provides three key views of a 3D shape. A depth panorama contains the complete 2.5D information of each view. 3V-DepthPano CNN is a CNN system with three branches designed for depth panoramas generated from the three key views. This recognition system, i.e., 3V-DepthPano CNN, applies a three-branch convolutional neural network to aggregate the 3D shape depth panorama information into a more compact 3D shape descriptor to implement the classification of 3D shapes. Furthermore, we adopt a fine-tuning technique on 3V-DepthPano CNN and extract shape features to facilitate the retrieval of 3D shapes. The proposed method implements a good tradeoff state between higher accuracy and training time. Experiments show that the proposed 3V-DepthPano CNN with 3 views obtains approximate accuracy to MVCNN with 12/80 views. But the 3V-DepthPano CNN frame takes much shorter time to obtain depth panoramas and train the network than MVCNN. It is superior to all other existing advanced methods for both classification and shape retrieval.