Wide and deep volumetric residual networks for volumetric image classification

2017/09/18 by Varun Arvind, Arvind, Varun, Anthony Costa +7
Computer Science · Earth and Planetary Sciences · Engineering · #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques

paper · pdf · doi:10.48550/arxiv.1710.01217

openalex publication_date 2017/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception models that are resource intensive. Residual neural networks have been demonstrated to be easier to optimize and do not suffer from vanishing/exploding gradients observed in deep networks. Here we implement a residual neural network for 3D object classification of the 3D Princeton ModelNet dataset. Further, we show that widening network layers dramatically improves accuracy in shallow residual nets, and residual neural networks perform comparable to state-of-the-art 3D shape net models, and we show that widening network layers improves classification accuracy. We provide extensive training and architecture parameters providing a better understanding of available network architectures for use in 3D object classification.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Image Synthesis With Auxiliary Classifier GANs

Related

• Volumetric and Multi-View CNNs for Object Classification on 3D Data
• FusionNet: 3D Object Classification Using Multiple Data Representations
• 3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks
• Deep Pyramidal Residual Networks
• Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks