PTINet: Converting 3D points to 2D images with deconvolution forpoint cloud classification
文献类型：会议论文
| 作者 | Fan Chen; Guoyuan Liang; Yimin Zhou; Xinyu Wu; Wei Feng; Shengzhan He |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Guangzhou,China |
| 英文摘要 | 3D point cloud classification is a very important task for many applications such as AR/VR, human-computer interaction, environment modeling, remote sensing, etc. Over the past decade, lots of research have been conducted and great progress been made in this field. However, point cloud classification is still a very challenging problem due to the irregularity of point cloud data, which makes the most popular deep neural network difficult to be applied. In order to get a regular 2D representation for point cloud data, some researchers projected point cloud data to 2D images by following some predefined rules, and utilized convolution for further processing. In this paper, we introduce a new deep network to find proper regular representations. The basic observation is that each point is considered as a 1x1 image. Therefore, deconvolution can be applied to map 3D points to 2D images. We named this network PTINet (Point to Image Network). Instead of predefined mapping rules, PTINet has the ability to find better mapping by learning, which can preserve 3D shape information as much as possible. The experiments conducted on the ModelNet dataset demonstrate competitive results of the proposed method. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13858]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Fan Chen,Guoyuan Liang,Yimin Zhou,et al. PTINet: Converting 3D points to 2D images with deconvolution forpoint cloud classification[C]. 见:. Guangzhou,China. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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