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Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm.
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X.; Funkhouser, T. A.; Guibas, L. J.; Hanrahan, P.; Huang, Q.; Li, Z.; Savarese, S.; Savva, M.; Song, S.; Su, H.; Xiao, J.; Yi, L.; and Yu, F. 2015 · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Wu, Z.; Song, S.; Khosla, A.; Yu, F.; Zhang, L.; Tang, X.; and Xiao, J. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Dai, A.; Chang, A. X.; Savva, M.; Halber, M.; Funkhouser, T.; and Nießner, M. 2017 · 2017
Earlier work this paper cites.
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H.; Su, H.; and Guibas, L. J. 2017 · 2017
Earlier work this paper cites.
Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 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.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 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
Earlier work this paper cites.
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
Earlier work this paper cites.
Weakly supervised 3d object detection from lidar point cloud
Meng, Q.; Wang, W.; Zhou, T.; Shen, J.; Van Gool, L.; and Dai, D. 2020 · 2020
Earlier work this paper cites.
Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
Philion, J.; and Fidler, S. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Xie, S.; Gu, J.; Guo, D.; Qi, C. R.; Guibas, L. J.; and Litany, O. 2020 · 2020
Cited alongside, same era.
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
Huang, J.; Huang, G.; Zhu, Z.; and Du, D. 2021 · 2021
Cited alongside, same era.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Liang, H.; Jiang, C.; Feng, D.; Chen, X.; Xu, H.; Liang, X.; Zhang, W.; Li, Z.; and Van Gool, L. 2021 · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Cited alongside, same era.
Center-based 3d object detection and tracking
Yin, T.; Zhou, X.; and Krahenbuhl, P. 2021 · 2021
Cited alongside, same era.
Masked Autoencoders for Point Cloud Self-supervised Learning
Pang, Y.; Wang, W.; Tay, F. E. H.; Liu, W.; Tian, Y.; and Yuan, L. 2022 · 2022
Closest in time.
SimMIM: a Simple Framework for Masked Image Modeling
Xie, Z.; Zhang, Z.; Cao, Y.; Lin, Y.; Bao, J.; Yao, Z.; Dai, Q.; and Hu, H. 2022 · 2022
Closest in time.
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection
Yin, J.; Zhou, D.; Zhang, L.; Fang, J.; Xu, C.; Shen, J.; and Wang, W. 2022 · 2022
Closest in time.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Yu, X.; Tang, L.; Rao, Y.; Huang, T.; Zhou, J.; and Lu, J. 2022 · 2022
Closest in time.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Zhang, R.; Guo, Z.; Gao, P.; Fang, R.; Zhao, B.; Wang, D.; Qiao, Y.; and Li, H. 2022 · 2022
Closest in time.
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Self-Supervised Pretraining of 3D Features on any Point-Cloud
Zhang, Z.; Girdhar, R.; Joulin, A.; and Misra, I. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Focal Sparse Convolutional Networks for 3D Object Detection
Chen, Y.; Li, Y.; Zhang, X.; Sun, J.; and Jia, J. 2022 · 2022
Cited alongside, same era.
Vista: Boosting 3d object detection via dual cross-view spatial attention
Deng, S.; Liang, Z.; Sun, L.; and Jia, K. 2022 · 2022
Cited alongside, same era.
Masked Autoencoders Are Scalable Vision Learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. B. 2022 · 2022
Cited alongside, same era.
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
Huang, J.; and Huang, G. 2022 · 2022
Cited alongside, same era.
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Liang, T.; Xie, H.; Yu, K.; Xia, Z.; Lin, Z.; Wang, Y.; Tang, T.; Wang, B.; and Tang, Z. 2022 · 2022
Cited alongside, same era.
ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation
Boulch, A.; Sautier, C.; Michele, B.; Puy, G.; and Marlet, R. 2023 · 2023
Closest in time.
Masked autoencoder for self-supervised pre-training on lidar point clouds
Hess, G.; Jaxing, J.; Svensson, E.; Hagerman, D.; Petersson, C.; and Svensson, L. 2023 · 2023
Closest in time.
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding
Jiang, L.; Yang, Z.; Shi, S.; Golyanik, V.; Dai, D.; and Schiele, B. 2023 · 2023
Closest in time.
LinK: Linear Kernel for LiDAR-Based 3D Perception
Lu, T.; Ding, X.; Liu, H.; Wu, G.; and Wang, L. 2023 · 2023
Closest in time.
GeoMAE: Masked Geometric Target Prediction for Self-Supervised Point Cloud Pre-Training
Tian, X.; Ran, H.; Wang, Y.; and Zhao, H. 2023 · 2023
Closest in time.
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training
Xu, R.; Wang, T.; Zhang, W.; Chen, R.; Cao, J.; Pang, J.; and Lin, D. 2023 · 2023
Closest in time.
GD-MAE: Generative Decoder for MAE Pre-Training on LiDAR Point Clouds
Yang, H.; He, T.; Liu, J.; Chen, H.; Wu, B.; Lin, B.; He, X.; and Ouyang, W. 2023 · 2023
Closest in time.