Five aspects of researches about automatic drive perception by Waymo

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  • Five aspects of researches about automatic drive perception by Waymo.
  • Main stream network model:

    • Pointpillar: Fast Encoders for Object Detection from Point Clouds by Lang et al.

    paper:https://arxiv.org/pdf/1812.05784.pdf

    • PV-RCNN++:Point-Voxel Feature Set Abstraction for 3D Object Detection by Shi et al.

    paper:https://arxiv.org/pdf/2102.00463.pdf

    Five aspects of researches about automatic drive perception by Waymo.

    SPG:Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

    • targeted & light-weight model for improving LIDAR input quality against occlusions and poor weathers

    • Effectiveness verified by applying to popular and SOTA 3D detectors.

    • complete the 3D shape before Detection

    paper:https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_SPG_Unsupervised_Domain_Adaptation_for_3D_Object_Detection_via_Semantic_ICCV_2021_paper.pdf

    3D-man:3D Multi-frame Attention Network for Object Detection

    • Complementary information from multiple frames

    • learned attention to fuse multi-frame

    • learn to fuse complementary information from multi-frame data via attention layers

    paper:https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_3D-MAN_3D_Multi-Frame_Attention_Network_for_Object_Detection_CVPR_2021_paper.pdf

    RSN: Range Sparse Net for Efficient,Accurate LIDAR 3D Object Detection

    • targeted feature selection via segmentation

    • Efficient backbone based on sparse conv

    paper:https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_RSN_Range_Sparse_Net_for_Efficient_Accurate_LiDAR_3D_Object_CVPR_2021_paper.pdf

    Labeling Automation: Offboard 3D Object Detection from Point Cloud Sequences

    • leverage structured information in the 3D space and temporal sequences.

    • Quality on-par with human labelers

    paper:https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.pdf

    SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving

    • Generating realistic camera images for novel SDC and dynamic object poses

    • Scalable solution based on pre-collected LIDAR and camera data

    paper:https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_SurfelGAN_Synthesizing_Realistic_Sensor_Data_for_Autonomous_Driving_CVPR_2020_paper.pdf

    版权声明:本文为CSDN博主「平山村小明」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
    原文链接:https://blog.csdn.net/weixin_46187561/article/details/121879797

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