.png){kind=icon}
.png){kind=icon}
Return
PointCutMix: Regularization strategy for point cloud classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2022.07.049.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data to alleviate those problems. It finds the one-to-one correspondence between two point clouds and generates new training data by replacing the points in one sample with their corresponding pairs. Two replacement strategies are proposed to adapt to the accuracy or robustness requirement for different tasks, one of which is to randomly select all replacing points while the other one is to select k nearest neighbors of a single random point. Both strategies consistently and significantly improve the performance of various models on point cloud clas-sification problems. By introducing the saliency maps to guide the selection of replacing points, the per-formance further improves. Since our method persists local semantic information of the points, we also at the first time extend MSDA for the point cloud segmentation problem. Moreover, PointCutMix is vali-dated to enhance the model's robustness. When using as a defense method, our method outperforms the state-of-the-art defense algorithms. The code is available at:https://github.com/cuge1995/ PointCutMix.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
3D computer vision
Point cloud
Deep learning
Data augmentation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W