文章目录[隐藏]
- Main stream network model:
- Pointpillar: Fast Encoders for Object Detection from Point Clouds by Lang et al.
- PV-RCNN++:Point-Voxel Feature Set Abstraction for 3D Object Detection by Shi et al.
- 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
- Labeling Automation: Offboard 3D Object Detection from Point Cloud Sequences
- SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
- SPG：Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
Main stream network model:
paper:https://arxiv.org/pdf/1812.05784.pdf
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
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
暂无评论