欢迎访问《图学学报》 分享到：
图学学报

图学学报

• 专论：第12届中国计算机图形学大会 (CHINAGRAPH 广州) • 上一篇    下一篇

基于体素特征重组网络的三维物体识别

  

  1. 1. 合肥工业大学计算机与信息学院 VCC 研究室，安徽 合肥 230601； 
    2. 工业安全与应急技术安徽省重点实验室(合肥工业大学)，安徽 合肥 230009； 
    3. 北德克萨斯大学计算机科学与工程学院，德克萨斯 丹顿 76201
  • 出版日期:2019-04-30 发布日期:2019-05-10
  • 基金资助:
    安徽省自然科学基金项目(1708085MF158)；国家自然科学基金项目(61602146)；国家留学基金项目(201706695044)；合肥工业大学智能制 造技术研究院科技成果转化及产业化重点项目(IMICZ2017010)

3D Object Recognition Based on Voxel Features Reorganization Network

  1. 1. VCC Division, School of Computer and Information, Hefei University of Technology, Hefei Anhui 230601, China; 
    2. Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology), Hefei Anhui 230009, China; 
    3. Department of Computer Science and Engineering, University of North Texas, Denton TX 76201, United States
  • Online:2019-04-30 Published:2019-05-10

PDF (PC)

175

摘要： 三维物体识别是计算机视觉领域近年来的研究热点，其在自动驾驶、医学影像处 理等方面具有重要的应用前景。针对三维物体的体素表达形式，特征重组卷积神经网络 VFRN 使用了直接连接同一单元中不相邻的卷积层的短连接结构。网络通过独特的特征重组方式，复 用并融合多维特征，提高特征表达能力，以充分提取物体结构特征。同时，网络的短连接结构 有利于梯度信息的传播，加之小卷积核和全局均值池化的使用，进一步提高了网络的泛化能力， 降低了网络模型的参数量和训练难度。ModelNet 数据集上的实验表明，VFRN 克服了体素数据 分辨率低和纹理缺失的问题，使用较少的参数取得了优于现有方法的识别准确率。

关键词: 物体识别, 体素, 卷积神经网络, 特征重组, 短连接

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