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LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other.
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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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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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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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R.; Su, H.; Mo, K.; and Guibas, L. J. 2017 · 2017
Earlier work this paper cites.
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Berman, M.; Rannen Triki, A.; and Blaschko, M. B. 2018 · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
Graham, B.; Engelcke, M.; and Van Der Maaten, L. 2018 · 2018
Earlier work this paper cites.
The 1cycle policy https://sgugger.github.io/the-1cycle-policy.html
Gugger, S. 2018 · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A.; Gal, Y.; and Cipolla, R. 2018 · 2018
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MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Teichmann, M.; Weber, M.; Zoellner, M.; Cipolla, R.; and Urtasun, R. 2018 · 2018
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Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Wu, B.; Wan, A.; Yue, X.; and Keutzer, K. 2018 · 2018
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Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
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Pixor: Real-time 3d object detection from point clouds
Yang, B.; Luo, W.; and Urtasun, R. 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
Earlier work this paper cites.
4d spatio-temporal convnets: Minkowski convolutional neural networks
Choy, C.; Gwak, J.; and Savarese, S. 2019 · 2019
Earlier work this paper cites.
Panoptic segmentation
Kirillov, A.; He, K.; Girshick, R.; Rother, C.; and Dollár, P. 2019 · 2019
Earlier work this paper cites.
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
Cited alongside, same era.
Nuscenes: A multimodal dataset for autonomous driving
Caesar, H.; Bankiti, V.; Lang, A. H.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020 · 2020
Cited alongside, same era.
Object as hotspots: An anchor-free 3d object detection approach via firing of hotspots
Chen, Q.; Sun, L.; Wang, Z.; Jia, K.; and Yuille, A. 2020 · 2020
Cited alongside, same era.
SalsaNext: Fast, uncertainty-aware semantic segmentation of LiDAR point clouds
Cortinhal, T.; Tzelepis, G.; and Erdal Aksoy, E. 2020 · 2020
Cited alongside, same era.
Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
Shi, S.; Guo, C.; Jiang, L.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2020 · 2020
Cited alongside, same era.
Efficientlps: Efficient lidar panoptic segmentation
Sirohi, K.; Mohan, R.; Büscher, D.; Burgard, W.; and Valada, A. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
Pointaugmenting: Cross-modal augmentation for 3d object detection
Wang, C.; Ma, C.; Zhu, M.; and Yang, X. 2021 · 2021
Later among the works it cites.
Object dgcnn: 3d object detection using dynamic graphs
Wang, Y.; and Solomon, J. M. 2021 · 2021
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Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation
Xu, J.; Zhang, R.; Dou, J.; Zhu, Y.; Sun, J.; and Pu, S. 2021 · 2021
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Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion
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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.
Searching efficient 3d architectures with sparse point-voxel convolution
Tang, H.; Liu, Z.; Zhao, S.; Lin, Y.; Lin, J.; Wang, H.; and Han, S. 2020 · 2020
Cited alongside, same era.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
Zhang, Y.; Zhou, Z.; David, P.; Yue, X.; Xi, Z.; Gong, B.; and Foroosh, H. 2020 · 2020
Cited alongside, same era.
Polarstream: Streaming object detection and segmentation with polar pillars
Chen, Q.; Vora, S.; and Beijbom, O. 2021 · 2021
Cited alongside, same era.
(AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Semantic Segmentation Network
Cheng, R.; Razani, R.; Taghavi, E.; Li, E.; and Liu, B. 2021 · 2021
Cited alongside, same era.
Rangedet: In defense of range view for lidar-based 3d object detection
Fan, L.; Xiong, X.; Wang, F.; Wang, N.; and Zhang, Z. 2021 · 2021
Cited alongside, same era.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Feng, D.; Zhou, Y.; Xu, C.; Tomizuka, M.; and Zhan, W. 2021 · 2021
Cited alongside, same era.
Yan, X.; Gao, J.; Li, J.; Zhang, R.; Li, Z.; Huang, R.; and Cui, S. 2021 · 2021
Later among the works it cites.
Center-based 3D Object Detection and Tracking
Yin, T.; Zhou, X.; and Krähenbühl, P. 2021 · 2021
Later among the works it cites.
Panoptic-PolarNet: Proposal-Free LiDAR Point Cloud Panoptic Segmentation
Zhou, Z.; Zhang, Y.; and Foroosh, H. 2021 · 2021
Later among the works it cites.
Transfusion: Robust lidar-camera fusion for 3d object detection with transformers
Bai, X.; Hu, Z.; Zhu, X.; Huang, Q.; Chen, Y.; Fu, H.; and Tai, C.-L. 2022 · 2022
Closest in time.
Embracing single stride 3d object detector with sparse transformer
Fan, L.; Pang, Z.; Zhang, T.; Wang, Y.-X.; Zhao, H.; Wang, F.; Wang, N.; and Zhang, Z. 2022 · 2022
Closest in time.
Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking
Fong, W. K.; Mohan, R.; Hurtado, J. V.; Zhou, L.; Caesar, H.; Beijbom, O.; and Valada, A. 2022 · 2022
Closest in time.
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
Closest in time.
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
Closest in time.
Ye, D.; Chen, W.; Zhou, Z.; Xie, Y.; Wang, Y.; Wang, P.; and Foroosh, H. 2022 · 2022
Closest in time.
CenterFormer: Center-based Transformer for 3D Object Detection
Zhou, Z.; Zhao, X.; Wang, Y.; Wang, P.; and Foroosh, H. 2022 · 2022
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BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation
Liu, Z.; Tang, H.; Amini, A.; Yang, X.; Mao, H.; Rus, D.; and Han, S. 2023 · 2023
Closest in time.