论文阅读：Multi-view Convolutional Neural Networks for 3D Shape Recognition
Preface 今天阅读一篇ICCV2015的论文：《Multi-view Convolutional Neural Networks for 3D Shape Recognition》，简称：MVCNN。 这里是Paper Homepage 这里是Paper Codegit 这篇文章的创新点在于： 用物体的三维数据从不一样“视角”所获得的二维渲染图，做为原始的训练数据。用经典、成熟的二维图像卷积
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相关文章
- 1. 论文阅读：Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 2. 【论文精读】Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 3. GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
- 4. MVCNN:Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 5. ICCV2015_Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 6. 论文：3D Convolutional Neural Networks for Human Action Recognition
- 7. 《3D Convolutional Neural Networks for Human Action Recognition》论文阅读笔记
- 8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
- 9. 论文阅读-Fast Algorithms for Convolutional Neural Networks
- 10. Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition论文阅读翻译