Fetching the paper…
Reading the bibliography…
Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding.
Shi, S.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2019b · 1907
Earlier work this paper cites.
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.; Vasudevan, V.; Han, W.; Ngiam, J.; Zhao, H.; Timofeev, A.; Ettinger, S.; Krivokon, M.; Gao, A.; Joshi, A.; Zhang, Y.; Shlens, J.; Chen, Z.; and Anguelov, D. 2019 · 1912
Earlier work this paper cites.
Std: Sparse-to-dense 3d object detector for point cloud
Yang, Z.; Sun, Y.; Liu, S.; Shen, X.; and Jia, J. 2019 · 1960
Earlier work this paper cites.
Yoo, J. H.; Kim, Y.; Kim, J. S.; and Choi, J. W. 2020 · 2004
Earlier work this paper cites.
Afdet: Anchor free one stage 3d object detection
Ge, R.; Ding, Z.; Hu, Y.; Wang, Y.; Chen, S.; Huang, L.; and Li, Y. 2020 · 2006
Earlier work this paper cites.
Pillar-based object detection for autonomous driving
Wang, Y.; Fathi, A.; Kundu, A.; Ross, D.; Pantofaru, C.; Funkhouser, T.; and Solomon, J. 2020 · 2007
Earlier work this paper cites.
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
Pang, S.; Morris, D.; and Radha, H. 2020 · 2009
Earlier work this paper cites.
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
Deng, J.; Shi, S.; Li, P.; Zhou, W.; Zhang, Y.; and Li, H. 2020 · 2012
Earlier work this paper cites.
Yan, X.; Gao, J.; Li, J.; Zhang, R.; Li, Z.; Huang, R.; and Cui, S. 2020 · 2012
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Geiger, A.; Lenz, P.; Stiller, C.; and Urtasun, R. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Earlier work this paper cites.
Sparse 3D convolutional neural networks
Graham, B. 2015 · 2015
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.
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.
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.
Joint 3d proposal generation and object detection from view aggregation
Ku, J.; Mozifian, M.; Lee, J.; Harakeh, A.; and Waslander, S. L. 2018 · 2018
Earlier work this paper cites.
Context-aware three-dimensional mean-shift with occlusion handling for robust object tracking in RGB-D videos
Liu, Y.; Jing, X.-Y.; Nie, J.; Gao, H.; Liu, J.; and Jiang, G.-P. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
Cited alongside, same era.
Occlusion-aware R-CNN: detecting pedestrians in a crowd
Zhang, S.; Wen, L.; Bian, X.; Lei, Z.; and Li, S. Z. 2018 · 2018
Cited alongside, same era.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y.; and Tuzel, O. 2018 · 2018
Cited alongside, same era.
Fast point r-cnn
Chen, Y.; Liu, S.; Shen, X.; and Jia, J. 2019 · 2019
Cited alongside, same era.
Epnet: Enhancing point features with image semantics for 3d object detection
Huang, T.; Liu, Z.; Chen, X.; and Bai, X. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Tanet: Robust 3d object detection from point clouds with triple attention
Liu, Z.; Zhao, X.; Huang, T.; Hu, R.; Zhou, Y.; and Bai, X. 2020 · 2020
Later among the works it cites.
Dops: Learning to detect 3d objects and predict their 3d shapes
Najibi, M.; Lai, G.; Kundu, A.; Lu, Z.; Rathod, V.; Funkhouser, T.; Pantofaru, C.; Ross, D.; Davis, L. S.; and Fathi, A. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning to see the invisible: End-to-end trainable amodal instance segmentation
Follmann, P.; König, R.; Härtinger, P.; Klostermann, M.; and Böttger, T. 2019 · 2019
Cited alongside, same era.
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.
Gs3d: An efficient 3d object detection framework for autonomous driving
Li, B.; Ouyang, W.; Sheng, L.; Zeng, X.; and Wang, X. 2019 · 2019
Cited alongside, same era.
Multi-task multi-sensor fusion for 3d object detection
Liang, M.; Yang, B.; Chen, Y.; Hu, R.; and Urtasun, R. 2019 · 2019
Cited alongside, same era.
Occlusion-net: 2d/3d occluded keypoint localization using graph networks
Reddy, N. D.; Vo, M.; and Narasimhan, S. G. 2019 · 2019
Cited alongside, same era.
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Wang, Z.; and Jia, K. 2019 · 2019
Cited alongside, same era.
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
Shi, S.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2020 · 2020
Later among the works it cites.
Point-gnn: Graph neural network for 3d object detection in a point cloud
Shi, W.; and Rajkumar, R. 2020 · 2020
Later among the works it cites.
Grid-gcn for fast and scalable point cloud learning
Xu, Q.; Sun, X.; Wu, C.-Y.; Wang, P.; and Neumann, U. 2020 · 2020
Later among the works it cites.
3dssd: Point-based 3d single stage object detector
Yang, Z.; Sun, Y.; Liu, S.; and Jia, J. 2020 · 2020
Later among the works it cites.
Hvnet: Hybrid voxel network for lidar based 3d object detection
Ye, M.; Xu, S.; and Cao, T. 2020 · 2020
Later among the works it cites.
Segvoxelnet: Exploring semantic context and depth-aware features for 3d vehicle detection from point cloud
Yi, H.; Shi, S.; Ding, M.; Sun, J.; Xu, K.; Zhou, H.; Wang, Z.; Li, S.; and Wang, G. 2020 · 2020
Later among the works it cites.
SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud
Li, Z.; Yao, Y.; Quan, Z.; Yang, W.; and Xie, J. 2021 · 2021
Closest in time.
MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square
Pan, Y.; Xiao, P.; He, Y.; Shao, Z.; and Li, Z. 2021 · 2021
Closest in time.
Occlusion Handling in Generic Object Detection: A Review
Saleh, K.; Szénási, S.; and Vámossy, Z. 2021 · 2021
Closest in time.
Spg: Unsupervised domain adaptation for 3d object detection via semantic point generation
Xu, Q.; Zhou, Y.; Wang, W.; Qi, C. R.; and Anguelov, D. 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.