ICCV2015_Multi-view Convolutional Neural Networks for 3D Shape Recognition
Author Hang Su 一作，PHD, 暂时就这篇比较有名 Abstract 对于3D的描述子，希望不是直接从3D数据中提取，而是基于view 本文第一个提出标准的CNN结构用3D render的view来训练模型从而识别3D物体 我们发现3D物体甚至可以通过single view来识别，越多view识别率越高 主要贡献： 提出一个新型结构，利用2D view来产生3D物体的描述子，并且
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相关文章
- 1. MVCNN:Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 2. ICCV2015_Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 3. GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
- 4. 【论文精读】Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 5. 论文阅读：Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 6. 3D Convolutional Neural Networks for Human Action Recognition
- 7. 译：3D Convolutional Neural Networks for Human Action Recognition
- 8. 论文：3D Convolutional Neural Networks for Human Action Recognition
- 9. 3DCNN:3D Convolutional Neural Networks for Human Action Recognition
- 10. Convolutional Neural Networks for Speech Recognition///2014