Residual Blocks PointNet: A novel faster PointNet framework for segmentation and estimated pose
文献类型：会议论文
| 作者 | Kai, Xu; Zhile, Yang; Yangjie, Xu; Liangbing, Feng |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 南京 |
| 英文摘要 | Given recent advances in Segmentation of Convolutional Neural Networks (CNNs), this paper aims to propose a more efficient structure which directly consumes point clouds for segmentation and estimated pose. ore specifically, a novel Residual Blocks PointNet is proposed providing a fast framework taking point sets as input and predicting 3D object part segmentation and 3D pose. The network of the proposed structure has been established composed of two subnet works: a key branch for 3D object part segmentation and the other branch for spatial transform to predict a 3D affine matrix. The major branch contains more residual blocks, which encapsulate shortcut connects with specified layer numbers, growth rate and conv(1*1)-bn-relu structure. The key point is the decrease of each level of network computing and the reuse of feature maps. The other is a parallel classification network for estimated pose with share portion weight except 3 groups of full connected layers. Empirically, Residual Blocks PointNet shows faster rate of convergence and acceptable performance. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14069]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Kai, Xu,Zhile, Yang,Yangjie, Xu,et al. Residual Blocks PointNet: A novel faster PointNet framework for segmentation and estimated pose[C]. 见:. 南京. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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