Work

PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval

Year: 2019

Type: conference-paper

Abstract: Point cloud based retrieval for place recognition is an emerging problem in vision field. The main challenge is how to find an efficient way to encode the local features into a discriminative global descriptor. In this paper, we propose a Point Contextual Attention Network (PCAN), which can predict the significance of each local point feature based on point context. Our network makes it possible to pay more attention to the task-relevent features when aggregating local features. Experiments on various benchmark datasets show that the proposed network can provide outperformance than current state-of-the-art approaches.

Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings

DOI: 10.1109/cvpr.2019.01272

Authors: Wenxiao Zhang, Chunxia Xiao

Institution: Wuhan University

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China, Wuhan Science and Technology Project

Awards: G21123358, G296457974, 面向散乱图片集可交互场景生成与编辑研究