Fetching the paper…
Reading the bibliography…
There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods.
Starnet: Targeted Computation for Object Detection in Point Clouds
Ngiam, J.; Caine, B.; Han, W.; Yang, B.; Chai, Y.; Sun, P.; Zhou, Y.; Yi, X.; Alsharif, O.; Nguyen, P.; et al. 2019 · 1908
Earlier work this paper cites.
Class-balanced Grouping and Sampling for Point Cloud 3D object Detection
Zhu, B.; Jiang, Z.; Zhou, X.; Li, Z.; and Yu, G. 2019 · 1908
Earlier work this paper cites.
1st Place Solution for Waymo Open Dataset Challenge–3D Detection and Domain Adaptation
Ding, Z.; Hu, Y.; Ge, R.; Huang, L.; Chen, S.; Wang, Y.; and Liao, J. 2020 · 2006
Earlier work this paper cites.
1st Place Solutions for Waymo Open Dataset Challenges–2D and 3D Tracking
Wang, Y.; Chen, S.; Huang, L.; Ge, R.; Hu, Y.; Ding, Z.; and Liao, J. 2020c · 2006
Earlier work this paper cites.
CenterNet3D: An Anchor free Object Detector for Autonomous Driving
Wang, G.; Tian, B.; Ai, Y.; Xu, T.; Chen, L.; and Cao, D. 2020b · 2007
Earlier work this paper cites.
Shi, S.; Guo, C.; Yang, J.; and Li, H. 2020b · 2008
Earlier work this paper cites.
Fast r-cnn
Girshick, R. 2015 · 2015
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Multi-View 3D Object Detection Network for Autonomous Driving
Chen, X.; Ma, H.; Wan, J.; Li, B.; and Xia, T. 2017 · 2017
Earlier work this paper cites.
Submanifold Sparse Convolutional Networks
Graham, B.; and van der Maaten, L. 2017 · 2017
Earlier work this paper cites.
Mask R-CNN
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
Earlier work this paper cites.
Focal Loss for Dense Object Detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
Earlier work this paper cites.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Qi, C. R.; Yi, L.; Su, H.; and Guibas, L. J. 2017 · 2017
Earlier work this paper cites.
Cascade R-CNN: Delving into High Quality Object Detection
Cai, Z.; and Vasconcelos, N. 2018 · 2018
Earlier work this paper cites.
Averaging Weights Leads to Wider Optima and Better Generalization
Izmailov, P.; Podoprikhin, D.; Garipov, T.; Vetrov, D.; and Wilson, A. G. 2018 · 2018
Earlier work this paper cites.
Acquisition of Localization Confidence for Accurate Object Detection
Jiang, B.; Luo, R.; Mao, J.; Xiao, T.; and Jiang, Y. 2018 · 2018
Earlier work this paper cites.
Cornernet: Detecting Objects as Paired Keypoints
Law, H.; and Deng, J. 2018 · 2018
Earlier work this paper cites.
SECOND: Sparsely Embedded Convolutional Detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
Earlier work this paper cites.
PIXOR: Real-time 3D Object Detection from Point Clouds
Yang, B.; Luo, W.; and Urtasun, R. 2018 · 2018
Earlier work this paper cites.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y.; and Tuzel, O. 2018 · 2018
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.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
Cited alongside, same era.
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
Meyer, G. P.; Laddha, A.; Kee, E.; Vallespi-Gonzalez, C.; and Wellington, C. K. 2019 · 2019
Cited alongside, same era.
Deep Hough Voting for 3D Object Detection in Point Clouds
Qi, C. R.; Litany, O.; He, K.; and Guibas, L. J. 2019 · 2019
Cited alongside, same era.
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
Shi, S.; Wang, X.; and Li, H. 2019 · 2019
Cited alongside, same era.
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
Yang, Z.; Sun, Y.; Liu, S.; Shen, X.; and Jia, J. 2019 · 2019
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
Closest in time.
Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness
Ge, R.; Ding, Z.; Hu, Y.; Shao, W.; Huang, L.; Li, K.; and Liu, Q. 2021 · 2021
Closest in time.
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Guan, T.; Wang, J.; Lan, S.; Chandra, R.; Wu, Z.; Davis, L.; and Manocha, D. 2021 · 2021
Closest in time.
The 1cycle policy
Gugger, S. 2018 · 2021
Closest in time.
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
Li, Z.; Wang, F.; and Wang, N. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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. H.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020 · 2020
Cited alongside, same era.
Improving 3d Object Detection Through Progressive Population based Augmentation
Cheng, S.; Leng, Z.; Cubuk, E. D.; Zoph, B.; Bai, C.; Ngiam, J.; Song, Y.; Caine, B.; Vasudevan, V.; Li, C.; et al. 2020 · 2020
Cited alongside, same era.
AFDet: Anchor Free One Stage 3D Object Detection
Ge, R.; Ding, Z.; Hu, Y.; Wang, Y.; Chen, S.; Huang, L.; and Li, Y. 2020 · 2020
Cited alongside, same era.
Structure Aware Single-stage 3D Object Detection from Point Cloud
He, C.; Zeng, H.; Huang, J.; Hua, X.-S.; and Zhang, L. 2020 · 2020
Cited alongside, same era.
What you see is what you get: Exploiting visibility for 3d object detection
Hu, P.; Ziglar, J.; Held, D.; and Ramanan, D. 2020 · 2020
Cited alongside, same era.
3rd Place Solution of Waymo Open Dataset Challenge 2021 Real-time 3D Detection Track
Liu, H.; Zheng, R.; Peng, J.; and Tian, L. 2021 · 2021
Closest in time.
Real-time 3D Object Detection using Feature Map Flow
Murhij, Y.; and Yudin, D. 2021 · 2021
Closest in time.
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection
Noh, J.; Lee, S.; and Ham, B. 2021 · 2021
Closest in time.
OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds
OpenPCDet Development Team. 2020 · 2021
Closest in time.
3d object detection with pointformer
Pan, X.; Xia, Z.; Song, S.; Li, L. E.; and Huang, G. 2021 · 2021
Closest in time.
Offboard 3D Object Detection from Point Cloud Sequences
Qi, C. R.; Zhou, Y.; Najibi, M.; Sun, P.; Vo, K.; Deng, B.; and Anguelov, D. 2021 · 2021
Closest in time.
Improving 3d object detection with channel-wise transformer
Sheng, H.; Cai, S.; Liu, Y.; Deng, B.; Huang, J.; Hua, X.-S.; and Zhao, M.-J. 2021 · 2021
Closest in time.
Shi, S.; Jiang, L.; Deng, J.; Wang, Z.; Guo, C.; Shi, J.; Wang, X.; and Li, H. 2021 · 2021
Closest in time.
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
Closest in time.
Multi-Stage Refinement Network for Point Cloud 3D Object Detection
Wang, F.; Li, Z.; Yan, Y.; and Wang, N. 2020a · 2021
Closest in time.
CenterAtt: Fast 2-stage Center Attention Network
Xu, J.; Tang, X.; Dou, J.; Shu, X.; and Zhu, Y. 2021 · 2021
Closest in time.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
Yang, Z.; Zhou, Y.; Chen, Z.; and Ngiam, J. 2021 · 2021
Closest in time.
CenterPoint++ Submission to the Waymo Real-time 3D Detection Challenge
Yin, T.; Zhou, X.; and Krahenbuhl, P. 2021b · 2021
Closest in time.
VarifocalNet: An IoU-aware Dense Object Detector
Zhang, H.; Wang, Y.; Dayoub, F.; and Sünderhauf, N. 2021 · 2021
Closest in time.
CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
Zheng, W.; Tang, W.; Chen, S.; Jiang, L.; and Fu, C.-W. 2021 · 2021
Closest in time.
Cylindrical and asymmetrical 3d convolution networks for lidar-based perception
Zhu, X.; Zhou, H.; Wang, T.; Hong, F.; Li, W.; Ma, Y.; Li, H.; Yang, R.; and Lin, D. 2021 · 2021
Closest in time.