ICCV2015_Multi-view Convolutional Neural Networks for 3D Shape Recognition
Author
Hang Su 一做，PHD, 暂时就这篇比较有名函数
Abstract
对于3D的描述子，但愿不是直接从3D数据中提取，而是基于view学习
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物体设计
咱们发现3D物体甚至能够经过single view来识别，越多view识别率越高3d
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，而且在任务上都取得了SOTAblog
同时设计了一种融合多种view信息的方法io
输入数据是 3D mesh效率
怎么生成 view: Phong reflection model (别人提出的模型)model
Retrievel
设计的目标函数方法
须要计算$n_x\timesn_y$次im
这样效率不高， 因此学习自动合成全部的view特征就会提升效率
Result
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相关文章
- 1. MVCNN:Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 2. GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
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- 5. 论文阅读：Multi-view Convolutional Neural Networks for 3D Shape Recognition
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- 7. 译：3D Convolutional Neural Networks for Human Action Recognition
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- 10. Convolutional Neural Networks for Speech Recognition///2014