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The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios.
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Zhu, B.; Jiang, Z.; Zhou, X.; Li, Z.; and Yu, G. 2019 · 1908
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Multi-view 3D object detection network for autonomous driving
Chen, X.; Ma, H.; Wan, J.; Li, B.; and Xia, T. 2017 · 1915
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Yolov4: Optimal speed and accuracy of object detection
Bochkovskiy, A.; Wang, C.-Y.; and Liao, H.-Y. M. 2020 · 2004
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Submanifold sparse convolutional networks
Graham, B.; and van der Maaten, L. 2017 · 2017
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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Frustum pointnets for 3D object detection from rgb-d data
Qi, C. R.; Liu, W.; Wu, C.; Su, H.; and Guibas, L. J. 2018 · 2018
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Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
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Voxelnet: End-to-end learning for point cloud based 3D object detection
Zhou, Y.; and Tuzel, O. 2018 · 2018
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Pointpillars: Fast encoders for object detection from point clouds
Lang, A. H.; Vora, S.; Caesar, H.; Zhou, L.; Yang, J.; and Beijbom, O. 2019 · 2019
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Deep hough voting for 3D object detection in point clouds
Qi, C. R.; Litany, O.; He, K.; and Guibas, L. J. 2019 · 2019
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Pointrcnn: 3D object proposal generation and detection from point cloud
Shi, S.; Wang, X.; and Li, H. 2019 · 2019
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Super-convergence: Very fast training of neural networks using large learning rates
Smith, L. N.; and Topin, N. 2019 · 2019
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Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
Bewley, A.; Sun, P.; Mensink, T.; Anguelov, D.; and Sminchisescu, C. 2020 · 2020
Cited alongside, same era.
Nuscenes: A multimodal dataset for autonomous driving
Caesar, H.; Bankiti, V.; Lang, A.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020 · 2020
Cited alongside, same era.
Voxel-FPN: Multi-scale voxel feature aggregation for 3D object detection from LIDAR point clouds
Kuang, H.; Wang, B.; An, J.; Zhang, M.; and Zhang, Z. 2020 · 2020
Cited alongside, same era.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P.; Kretzschmar, H.; Dotiwalla, X.; Chouard, A.; Patnaik, V.; Tsui, P.; Guo, J.; Zhou, Y.; Chai, Y.; Caine, B.; et al. 2020 · 2020
Cited alongside, same era.
3dssd: Point-based 3D single stage object detector
Yang, Z.; Sun, Y.; Liu, S.; and Jia, J. 2020 · 2020
Cited alongside, same era.
Spconv: Spatially Sparse Convolution Library
Contributors, S. 2022 · 2022
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VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention
Deng, S.; Liang, Z.; Sun, L.; and Jia, K. 2022 · 2022
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A versatile multi-view framework for lidar-based 3d object detection with guidance from panoptic segmentation
Fazlali, H.; Xu, Y.; Ren, Y.; and Liu, B. 2022 · 2022
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YOLOv5 release v6.1
Glenn, J. 2022 · 2022
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Afdetv2: Rethinking the necessity of the second stage for object detection from point clouds
Hu, Y.; Ding, Z.; Ge, R.; Shao, W.; Huang, L.; Li, K.; and Liu, Q. 2022 · 2022
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Swformer: Sparse window transformer for 3d object detection in point clouds
Pei Sun, W. W. C. L. F. X. Z. L., Mingxing Tan; and Anguelov, D. 2022 · 2022
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Zheng, Z.; Wang, P.; Liu, W.; Li, J.; Ye, R.; and Ren, D. 2020 · 2020
Cited alongside, same era.
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
Deng, J.; Shi, S.; Li, P.; Zhou, W.; Zhang, Y.; and Li, H. 2021 · 2021
Cited alongside, same era.
Repvgg: Making vgg-style convnets great again
Ding, X.; Zhang, X.; Ma, N.; Han, J.; Ding, G.; and Sun, J. 2021 · 2021
Cited alongside, same era.
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
Mao, J.; Niu, M.; Bai, H.; Liang, X.; Xu, H.; and Xu, C. 2021 · 2021
Cited alongside, same era.
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Sun, P.; Wang, W.; Chai, Y.; Elsayed, G.; Bewley, A.; Zhang, X.; Sminchisescu, C.; and Anguelov, D. 2021 · 2021
Cited alongside, same era.
Pointaugmenting: Cross-modal augmentation for 3d object detection
Wang, C.; Ma, C.; Zhu, M.; and Yang, X. 2021 · 2021
Cited alongside, same era.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
Yang, Z.; Zhou, Y.; Chen, Z.; and Ngiam, J. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection
Shi, G.; Li, R.; and Ma, C. 2022 · 2022
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PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
Shi, S.; Jiang, L.; Deng, J.; Wang, Z.; Guo, C.; Shi, J.; Wang, X.; and Li, H. 2022 · 2022
Later among the works it cites.
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
Tian, Z.; Chu, X.; Wang, X.; Wei, X.; and Shen, C. 2022 · 2022
Later among the works it cites.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y.; Bochkovskiy, A.; and Liao, H.-Y. M. 2022 · 2022
Later among the works it cites.
PP-YOLOE: An evolved version of YOLO
Xu, S.; Wang, X.; Lv, W.; Chang, Q.; Cui, C.; Deng, K.; Wang, G.; Dang, Q.; Wei, S.; Du, Y.; et al. 2022 · 2022
Later among the works it cites.
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Chen, Y.; Liu, J.; Zhang, X.; Qi, X.; and Jia, J. 2023 · 2023
Closest in time.
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds
Li, J.; Luo, C.; and Yang, X. 2023 · 2023
Closest in time.