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3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 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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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Multi-level fusion based 3d object detection from monocular images
Xu, B.; and Chen, Z. 2018 · 2018
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An energy and GPU-computation efficient backbone network for real-time object detection
Lee, Y.; Hwang, J.-w.; Lee, S.; Bae, Y.; and Park, J. 2019 · 2019
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Accurate monocular 3d object detection via color-embedded 3d reconstruction for autonomous driving
Ma, X.; Wang, Z.; Li, H.; Zhang, P.; Ouyang, W.; and Fan, X. 2019 · 2019
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Orthographic feature transform for monocular 3d object detection
Roddick, T.; Kendall, A.; and Cipolla, R. 2019 · 2019
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Fcos: Fully convolutional one-stage object detection
Tian, Z.; Shen, C.; Chen, H.; and He, T. 2019 · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Wang, Y.; Chao, W.-L.; Garg, D.; Hariharan, B.; Campbell, M.; and Weinberger, K. Q. 2019 · 2019
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Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
You, Y.; Wang, Y.; Chao, W.-L.; Garg, D.; Pleiss, G.; Hariharan, B.; Campbell, M.; and Weinberger, K. Q. 2019 · 2019
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Objects as points
Zhou, X.; Wang, D.; and Krähenbühl, P. 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
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
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Cited alongside, same era.
MMDetection3D: OpenMMLab next-generation platform for general 3D object detection
Contributors, M. 2020 · 2020
Cited alongside, same era.
Learning depth-guided convolutions for monocular 3d object detection
Ding, M.; Huo, Y.; Yi, H.; Wang, Z.; Shi, J.; Lu, Z.; and Luo, P. 2020 · 2020
Cited alongside, same era.
3D Packing for Self-Supervised Monocular Depth Estimation
Guizilini, V.; Ambrus, R.; Pillai, S.; Raventos, A.; and Gaidon, A. 2020 · 2020
Cited alongside, same era.
Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
HDMapNet: A Local Semantic Map Learning and Evaluation Framework
Li, Q.; Wang, Y.; Wang, Y.; and Zhao, H. 2021 · 2021
Later among the works it cites.
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Park, D.; Ambrus, R.; Guizilini, V.; Li, J.; and Gaidon, A. 2021 · 2021
Later among the works it cites.
It’s All Around You: Range-Guided Cylindrical Network for 3D Object Detection
Rapoport-Lavie, M.; and Raviv, D. 2021 · 2021
Later among the works it cites.
Categorical depth distribution network for monocular 3d object detection
Reading, C.; Harakeh, A.; Chae, J.; and Waslander, S. L. 2021 · 2021
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Fcos3d: Fully convolutional one-stage monocular 3d object detection
Wang, T.; Zhu, X.; Pang, J.; and Lin, D. 2021 · 2021
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Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation
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Philion, J.; and Fidler, S. 2020 · 2020
Cited alongside, same era.
Predicting semantic map representations from images using pyramid occupancy networks
Roddick, T.; and Cipolla, R. 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.
Structured Bird’s-Eye-View Traffic Scene Understanding from Onboard Images
Can, Y. B.; Liniger, A.; Paudel, D. P.; and Van Gool, L. 2021 · 2021
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.
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Chitta, K.; Prakash, A.; and Geiger, A. 2021 · 2021
Cited alongside, same era.
FIERY: Future Instance Prediction in Bird’s-Eye View From Surround Monocular Cameras
Hu, A.; Murez, Z.; Mohan, N.; Dudas, S.; Hawke, J.; Badrinarayanan, V.; Cipolla, R.; and Kendall, A. 2021 · 2021
Cited alongside, same era.
Yang, W.; Li, Q.; Liu, W.; Yu, Y.; Ma, Y.; He, S.; and Pan, J. 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.
Bevdet4d: Exploit temporal cues in multi-camera 3d object detection
Huang, J.; and Huang, G. 2022 · 2022
Closest in time.
BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
Li, Z.; Wang, W.; Li, H.; Xie, E.; Sima, C.; Lu, T.; Yu, Q.; and Dai, J. 2022 · 2022
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Learning Ego 3D Representation as Ray Tracing
Lu, J.; Zhou, Z.; Zhu, X.; Xu, H.; and Zhang, L. 2022 · 2022
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Translating Images into Maps
Saha, A.; Maldonado, O. M.; Russell, C.; and Bowden, R. 2022 · 2022
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Mˆ 2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
Xie, E.; Yu, Z.; Zhou, D.; Philion, J.; Anandkumar, A.; Fidler, S.; Luo, P.; and Alvarez, J. M. 2022 · 2022
Closest in time.