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Sparse 3D Point Clouds Segmentation Considering 2D Image Feature Extraction with Deep Learning

Yusheng LiYong TianJiandong Tian

College of Physics and Optoelectronic Engineering,Shenzhen University

摘要:Three-dimensional(3 D) point cloud segmentation plays an important role in autonomous navigation systems,such as mobile robots and autonomous cars.However,the segmentation is challenging because of data sparsity,uneven sampling density,irregular format,and lack of color texture.In this paper,we propose a sparse 3 D point cloud segmentation method based on 2 D image feature extraction with deep learning.Firstly,we jointly calibrate the camera and lidar to get the external parameters(rotation matrix and translation vector).Then,we introduce the Convolutional Neural Network(CNN)-based object detectors to generate 2 D object region proposals in the RGB image and classify object.Finally,based on the external parameters of joint calibration,we extract point clouds that can be projected to 2 D object region from 16-lines RS-LIDAR-16 scanner,and further fine segmentation in the extracted point cloud according to prior knowledge of the classification features.Experiments demonstrate the effectiveness of the proposed sparse point cloud segmentation method.
会议名称:

2019第十一届数字图像处理国际会议

会议时间:

2019-05-10

会议地点:

中国广东广州

  • 专辑:

    信息科技

  • 专题:

    计算机软件及计算机应用

  • DOI:

    10.26914/c.cnkihy.2019.007635

  • 分类号:

    TP391.41

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