Learning the Global Descriptor for 3-D Object Recognition Based on Multiple Views Decomposition

The key point of view based strategies for the analysis of 3D object is to obtain a global descriptor from a collection of its rendered views on 2D images. The views are always redundantly sampled as to ensure the completeness of the information. In this paper, we bring new insight into the study of multi-view object recognition, which models an object as a View Mixture Model (VMM). We argue that each object represented by the multiple views can be decomposed into just a few latent views. Based on the VMM, we introduce a decomposition module to mine the representations of these latent views for the construction of a compact and comprehensive descriptor. After that, we further propose a view alignment module to ensure the descriptor is robust to the variation of view permutation. We evaluate our method on the ModelNet-40, ModelNet-10 and ShapeNetCore55 datasets. The experimental results show that our method can learn efficient and comprehensive representation for 3D objects, and achieves state-of-the-art performance on both the 3D object classification and retrieval tasks. Lastly, experiments are conducted for benchmarking various popular CNN backbones on the 3D object recognition task, with a view to achieving fair comparisons and promoting the future research in this area. Codes for our paper are released: “
https://github.com/hjjpku/multi_view_sort
”.