Abstract: 3D object recognition is a research focus in the field of computer vision and has significant application prospect in automatic driving, medical image processing, etc. Aiming at voxel expression form of 3D object, VFRN (voxel features reorganization network), using short connection structure, directly connects non-adjacent convolutional layers in the same unit. Through unique feature recombination, the network reuses and integrates multi-dimensional features to improve the feature expression ability to fully extract the structural features of objects. At the same time, the short connection structure of the network is conducive to the spread of gradient information. Additionally, employing small convolution kernel and global average pooling not only enhances generalization capacity of network, but also reduces the parameters in network models and the training difficulty. The experiment on ModelNet data set indicates that VFRN overcomes problems including low resolution ratio in voxel data and texture deletion, and achieves better recognition accuracy rate using less parameter.

Key words: object recognition, voxel, convolution neural network, feature reorganization, short connection