Semantics feature sampling for point-based 3D object detection

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2024-07-23 DOI:10.1016/j.imavis.2024.105180

Jing-Dong Huang, Ji-Xiang Du, Hong-Bo Zhang, Huai-Jin Liu

引用次数: 0

Abstract

Currently, 3D object detection is a research hotspot in the field of computer vision. In this paper, we have observed that the commonly used set abstraction module retains excessive irrelevant background information during downsampling, impacting object detection precision. To address this, we propose a mixed sampling method. During point feature extraction, we integrate semantic features into the sampling process, guiding the set abstraction module to sample foreground points. In order to leverage the high-quality 3D proposals generated in the first stage, we have developed a virtual point pooling module aimed at acquiring the features of these proposals. This module facilitates the capture of more comprehensive and resilient ROI features. Experimental results on the KITTI test set show a 3.51% higher Average Precision (AP) compared to the PointRCNN baseline, particularly for moderately challenging car classes, highlighting the effectiveness of our approach.

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基于点的三维物体检测的语义特征采样

目前，三维物体检测是计算机视觉领域的研究热点。在本文中，我们发现常用的集合抽象模块在下采样过程中会保留过多无关背景信息，从而影响物体检测精度。为此，我们提出了一种混合采样方法。在点特征提取过程中，我们将语义特征整合到采样过程中，引导集合抽象模块对前景点进行采样。为了充分利用第一阶段生成的高质量 3D 提议，我们开发了一个虚拟点池模块，旨在获取这些提议的特征。该模块有助于获取更全面、更有弹性的 ROI 特征。在 KITTI 测试集上的实验结果表明，与 PointRCNN 基线相比，我们的平均精确度（AP）提高了 3.51%，尤其是在中等难度的汽车类别上，这凸显了我们方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.