LiDAR 3D Object Detection

Papers

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1609.06666

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

• intro: Apple Inc
• arxiv: https://arxiv.org/abs/1711.06396

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

• intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
• arxiv: https://arxiv.org/abs/1803.06199

Focal Loss in 3D Object Detection

• intro: IEEE RA-L 2019
• project page: https://sites.google.com/view/fl3d
• arxiv: https://arxiv.org/abs/1809.06065
• github: https://github.com/pyun-ram/FL3D

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1812.04244
• github(official): https://github.com/sshaoshuai/PV-RCNN
• github(official): https://github.com/sshaoshuai/PointRCNN

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1901.02237

3D Backbone Network for 3D Object Detection

https://arxiv.org/abs/1901.08373

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

https://arxiv.org/abs/1904.07537

Point-Voxel CNN for Efficient 3D Deep Learning

• intro: NeurIPS 2019 Spotlight
• project page: https://hanlab.mit.edu/projects/pvcnn/
• arxiv: https://arxiv.org/abs/1907.03739
• github: https://github.com/mit-han-lab/pvcnn

IoU Loss for 2D/3D Object Detection

• intro: 3d vision 2019
• arxiv: https://arxiv.org/abs/1908.03851

Deep Hough Voting for 3D Object Detection in Point Clouds

• intro: ICCV 2019
• intro: Facebook AI Research & Stanford University
• keywords: VoteNet
• arxiv: https://arxiv.org/abs/1904.09664
• github: https://github.com/facebookresearch/votenet

Fast Point R-CNN

• intro: ICCV 2019
• intro: CUHK & Tencent YouTu Lab
• arxiv: https://arxiv.org/abs/1908.02990

Interpolated Convolutional Networks for 3D Point Cloud Understanding

• intro: ICCV 2019
• arxiv: https://arxiv.org/abs/1908.04512

PointPillars: Fast Encoders for Object Detection from Point Clouds

• intro: nuTonomy: an APTIV company
• keywords: a single stage
• arxiv: http://http://arxiv.org/abs/1812.05784
• github(official): https://github.com/nutonomy/second.pytorch

LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving

• intro: CVPR 2019
• intro: Uber Advanced Technologies Group
• arxiv: https://arxiv.org/abs/1903.08701

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

• intro: CVPR Workshop on Autonomous Driving 2019
• keywords: LaserNet++
• arxiv: https://arxiv.org/abs/1904.11466

Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

• intro: TPAMI 2020
• arxiv: https://arxiv.org/abs/1907.03670
• github(official): https://github.com/sshaoshuai/PartA2-Net
• github(official): https://github.com/open-mmlab/OpenPCDet

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

• intro: CVPR 2019
• intro: winner of nuScenes 3D Object Detection challenge in WAD
• arxiv: https://arxiv.org/abs/1908.09492
• github: https://github.com/ZhengWG/Class-balanced-Grouping-and-Sampling-for-Point-Cloud-3D-Object-Detection

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

• intro: CoRL 2019
• intro: Waymo LLC & Google Brain
• keywords: dynamic voxelization
• arxiv: https://arxiv.org/abs/1910.06528

SampleNet: Differentiable Point Cloud Sampling

• intro: CVPR 2020 oral
• intro: Tel Aviv University
• arxiv: https://arxiv.org/abs/1912.03663
• github: https://github.com/itailang/SampleNet

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

• intro: AAAI 2020 oral
• intro: Huazhong University of Science and Technology & Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/1912.05163
• github: https://github.com/happinesslz/TANet

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/1912.13192
• github(official): https://github.com/open-mmlab/OpenPCDet

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

• intro: CVPR 2020
• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2003.01251
• github: https://github.com/WeijingShi/Point-GNN

PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges

• arxiv: https://arxiv.org/abs/2008.12599
• github: https://github.com/open-mmlab/OpenPCDet

Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

• intro: ECCV 2020 Workshop on Perception for Autonomous Driving
• intro: University of Waterloo
• arxiv: https://arxiv.org/abs/2008.08766
• github: https://github.com/AutoVision-cloud/Deformable-PV-RCNN

PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection

https://arxiv.org/abs/2208.13414

3DSSD: Point-based 3D Single Stage Object Detector

• intro: CVPR 2020 Oral
• arxiv: https://arxiv.org/abs/2002.10187
• github: https://github.com/Jia-Research-Lab/3DSSD

HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

• intro: CVPR 2020
• intro: DEEPROUTE.AI
• arxiv: https://arxiv.org/abs/2003.00186
• paper: http://openaccess.thecvf.com/content_CVPR_2020/papers/Ye_HVNet_Hybrid_Voxel_Network_for_LiDAR_Based_3D_Object_Detection_CVPR_2020_paper.pdf

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

• intro: ECCV 2020
• intro: CUHK & SenseTime Research & Hong Kong Baptist University
• arxiv: https://arxiv.org/abs/2004.02774
• github(mmdetection3d): https://github.com/xinge008/SSN

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

• intro: Google Research & Waymo LLC
• keywords: Range Conditioned Dilation (RCD)
• arxiv: https://arxiv.org/abs/2005.09927

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

• intro: University of Waterloo & Sun Yat-Sen University & Xilinx Technology & Ryerson University
• arxiv: https://arxiv.org/abs/2005.09830

Structure Aware Single-stage 3D Object Detection from Point Cloud

• intro: CVPR 2020
• intro: The Hong Kong Polytechnic University & DAMO Academy, Alibaba Group
• intro: SA-SSD
• paper: http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Structure_Aware_Single-Stage_3D_Object_Detection_From_Point_Cloud_CVPR_2020_paper.pdf
• paper: https://www4.comp.polyu.edu.hk/~cslzhang/paper/SA-SSD.pdf
• github: https://github.com/skyhehe123/SA-SSD

Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection

• intro: CVPR 2020
• intro: Fudan University & Baidu Inc. & University of Science and Technology of China
• arxiv: https://arxiv.org/abs/2006.04356

SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

https://arxiv.org/abs/2006.04043

Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection

https://arxiv.org/abs/2006.05187

Generative Sparse Detection Networks for 3D Single-shot Object Detection

• intro: Stanford University & NVIDIA
• arxiv: https://arxiv.org/abs/2006.12356

Local Grid Rendering Networks for 3D Object Detection in Point Clouds

https://arxiv.org/abs/2007.02099

InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

• intro: University of Maryland & Salesforce Research
• arxiv: https://arxiv.org/abs/2007.08556

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

• intro: ECCV 2020
• intro: Huazhong University of Science and Technology
• arxiv: https://arxiv.org/abs/2007.08856
• github: https://github.com/happinesslz/EPNet

Pillar-based Object Detection for Autonomous Driving

• intro: ECCV 2020
• intro: MIT & Google
• arxiv: https://arxiv.org/abs/2007.10323
• github(TensorFlow): https://github.com/WangYueFt/pillar-od

Weakly Supervised 3D Object Detection from Lidar Point Cloud

• intro: ECCV 2020
• intro: Beijing Institute of Technology & ETH Zurich & Inception Institute of Artificial Intelligence
• arxiv: https://arxiv.org/abs/2007.11901
• github: https://github.com/hlesmqh/WS3D

An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

• intro: ECCV 2020
• intro: Google Research
• arxiv: https://arxiv.org/abs/2007.12392

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

https://arxiv.org/abs/2007.13373

Weakly Supervised 3D Object Detection from Point Clouds

• intro: ACM MM 2020
• intro: MIT & Microsoft Research
• arxiv: https://arxiv.org/abs/2007.13970
• github: https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

• intro: ECCV 2020
• arxiv: https://arxiv.org/abs/2007.16100

Global Context Aware Convolutions for 3D Point Cloud Understanding

https://arxiv.org/abs/2008.02986

DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

• intro: IROS 2020
• arxiv: https://arxiv.org/abs/2008.08136
• github: https://github.com/dfki-av/DeepLiDARFlow

PointMixup: Augmentation for Point Clouds

• intro: ECCV 2020 spotlight
• arxiv: https://arxiv.org/abs/2008.06374

Cross-Modality 3D Object Detection

• intro: WACV 2021
• arxiv: https://arxiv.org/abs/2008.10436

LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks

https://arxiv.org/abs/2008.10309

Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

• intro: Uber Advanced Technologies Group
• arxiv: https://arxiv.org/abs/2008.11901

DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with Dynamic Voxelization and 3D Group Convolution

• intro: DESR Lab, Hong Kong University of Science and Technology
• arxiv: https://arxiv.org/abs/2009.02918

Joint Pose and Shape Estimation of Vehicles from LiDAR Data

• intro: Argo AI & Microsoft & Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2009.03964

Deep Learning for 3D Point Cloud Understanding: A Survey

• arxiv: https://arxiv.org/abs/2009.08920
• github: https://github.com/SHI-Labs/3D-Point-Cloud-Learning

Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

• intro: ECCV 2020 Workshop on Perception for Autonomous Driving
• intro: MIT & Google & Stanford
• arxiv: https://arxiv.org/abs/2009.11859

Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds

• arxiv: https://arxiv.org/abs/2010.04642
• github: https://github.com/nicolas-chaulet/torch-points3d

MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving

• intro: The Hong Kong University of Science and Technology
• project page: https://ram-lab.com/file/site/mlod/
• arxiv: https://arxiv.org/abs/2010.11702

StrObe: Streaming Object Detection from LiDAR Packets

• intro: CoRL 2020
• intro: Uber Advanced Technologies Group & University of Toronto
• arxiv: https://arxiv.org/abs/2011.06425

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models

• intro: NeurIPS 2020
• intro: Uber Advanced Technologies Group & University of Waterloo & University of Toronto
• arxiv: https://arxiv.org/abs/2011.07590

LiDAR-based Panoptic Segmentation via Dynamic Shifting Network

• intro: Nanyang Technological University & Chinese University of Hong Kong
• intro: Rank 1st place in the leaderboard of SemanticKITTI Panoptic Segmentation (accessed at 2020-11-16)
• arxiv: https://arxiv.org/abs/2011.11964
• github: https://github.com/hongfz16/DS-Net

CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud

• intro: AAAI 2021
• intro: The Chinese University of Hong Kong
• arxiv: https://arxiv.org/abs/2012.03015
• github: https://github.com/Vegeta2020/CIA-SSD

PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection

• intro: Beihang University
• arxiv: https://arxiv.org/abs/2012.10412

Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device

https://arxiv.org/abs/2012.13801

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

• intro: AAAI 2021
• arxiv: https://arxiv.org/abs/2012.15712
• github: https://github.com/djiajunustc/Voxel-R-CNN

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

• arxiv: https://arxiv.org/abs/2012.15072
• github: https://github.com/Banconxuan/RTS3D

Self-Attention Based Context-Aware 3D Object Detection

• intro: University of Waterloo
• arxiv: https://arxiv.org/abs/2101.02672
• github: https://github.com/AutoVision-cloud/SA-Det3D

A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding

• arxiv: https://arxiv.org/abs/2103.04056
• github: https://github.com/frankfengdi/LidarMTL

ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

• intro: CVPR 2021
• arxiv: https://arxiv.org/abs/2103.05346
• github: https://github.com/CVMI-Lab/ST3D

RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection

• intro: ICCV 2021
• arxiv: https://arxiv.org/abs/2103.10039
• github: https://github.com/TuSimple/RangeDet

Stereo CenterNet based 3D Object Detection for Autonomous Driving

https://arxiv.org/abs/2103.11071

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

• intro: CVPR 2021
• intro: TuSimple
• arxiv: https://arxiv.org/abs/2103.15297
• github: https://github.com/tusimple/LiDAR_RCNN

HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection

• intro: CVPR 2021 https://arxiv.org/abs/2104.00902

Group-Free 3D Object Detection via Transformers

• intro: University of Science and Technology of China & Microsoft Research Asia
• arxiv: https://arxiv.org/abs/2104.00678
• github: https://github.com/zeliu98/Group-Free-3D

SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

• intro: CVPR 2021
• arxiv: https://arxiv.org/abs/2104.09804
• github: https://github.com/Vegeta2020/SE-SSD

BEVDetNet: Bird’s Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving

https://arxiv.org/abs/2104.10780

Investigating Attention Mechanism in 3D Point Cloud Object Detection

• intro: Australian National University & Data61-CSIRO, Australia & University of Technology Sydney & Nanyang Technological University
• arxiv: https://arxiv.org/abs/2108.00620
• github: https://github.com/ShiQiu0419/attentions_in_3D_detection

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

• intro: ICCV 2021
• intro: The Chinese University of Hong Kong & Huawei Noah’s Ark Lab & HKUST & Sun Yat-Sen University
• arxiv: https://arxiv.org/abs/2109.02499

Voxel Transformer for 3D Object Detection

• intro: ICCV 2021
• intro: The Chinese University of Hong Kong & National University of Singapore & Huawei Noah’s Ark Lab & HKUST & Sun Yat-Sen University
• arxiv: https://arxiv.org/abs/2109.02497

A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation

• intro: CVPR 2022
• arxiv: https://arxiv.org/abs/2203.02133

Point Density-Aware Voxels for LiDAR 3D Object Detection

• intro: CVPR 2022
• arxiv: https://arxiv.org/abs/2203.05662
• github: https://github.com/TRAILab/PDV

VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention

• intro: CVPR 2022
• arxiv: https://arxiv.org/abs/2203.09704
• github: https://github.com/Gorilla-Lab-SCUT/VISTA

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds

• intro: CVPR 2022
• intro: National University of Defense Technology & University of Oxford
• arxiv: https://arxiv.org/abs/2203.11139
• github: https://github.com/yifanzhang713/IA-SSD

LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

• intro: Tsinghua University & State Key Lab of Intelligent Technologies and Systems & Gaussian Robotics
• arxiv: https://arxiv.org/abs/2203.14956
• github: https://github.com/weiyithu/LiDAR-Distillation

Point2Seq: Detecting 3D Objects as Sequences

• intro: CVPR 2022
• arxiv: https://arxiv.org/abs/2203.13394
• github: https://github.com/ocNflag/point2seq

OccAM’s Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

• intro: CVPR 2022
• arxiv: https://arxiv.org/abs/2204.06577
• github: https://github.com/dschinagl/occam

PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

• intro: Tsinghua University & Xi’an Jiaotong University & DIDI
• arxiv: https://arxiv.org/abs/2205.11098
• github: https://github.com/RunpeiDong/PointDistiller

PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection

• intro: Harbin Institute of Technology & Shanghai Jiao Tong University
• arxiv: https://arxiv.org/abs/2205.07403

Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images

• intro: Meituan Inc. & Zhejiang University & Northwestern Polytechnical University
• arxiv: https://arxiv.org/abs/2205.13764

Voxel Field Fusion for 3D Object Detection

• intro: CVPR 2022
• intro: The Chinese University of Hong Kong & The University of Hong Kong & MEGVII Technology & SmartMore
• arxiv: https://arxiv.org/abs/2205.15938
• github: https://github.com/dvlab-research/VFF

Unifying Voxel-based Representation with Transformer for 3D Object Detection

• intro: The Chinese University of Hong Kong & The University of Hong Kong & MEGVII Technology & SmartMore4
• arxiv: https://arxiv.org/abs/2206.00630
• github: https://github.com/dvlab-research/UVTR

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network

• intro: TuSimple & University of Central Florida
• intro: Official 1st Place Solution for the Waymo Open Dataset Challenges 2022 - 3D Semantic Segmentation
• arxiv: https://arxiv.org/abs/2206.11428

Rethinking IoU-based Optimization for Single-stage 3D Object Detection

• intro: ECCV 2022
• intro: Zhejiang University & Alibaba Cloud Computing Ltd. & National University of Singapore
• arxiv: https://arxiv.org/abs/2207.09332
• github: https://github.com/hlsheng1/RDIoU

Embracing Single Stride 3D Object Detector with Sparse Transformer

• intro: CVPR 2022
• intro: CASIA & UIUC & CMU & THU & TuSimple
• arxiv: https://arxiv.org/abs/2112.06375
• github: https://github.com/TuSimple/SST
• zhihu: https://zhuanlan.zhihu.com/p/476056546

Fully Sparse 3D Object Detection

• intro: ECCV 2022
• intro: CASIA & TuSimple
• arxiv: https://arxiv.org/abs/2207.10035
• github: https://github.com/TuSimple/SST

Anchor-free 3D Detection

Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots

• intro: Samsung Inc & Johns Hopkins University & South China University of Technology
• keywords: Object as Hotspots (OHS)
• arxiv: https://arxiv.org/abs/1912.12791

CenterNet3D: An Anchor free Object Detector for Autonomous Driving

• keywords: Non-Maximum Suppression free
• arxiv: https://arxiv.org/abs/2007.07214
• github: https://github.com/wangguojun2018/CenterNet3d

AFDet: Anchor Free One Stage 3D Object Detection

• intro: Horizon Robotics
• intro: CVPR Workshop 2020
• intro: Baseline detector for the 1st place solutions of Waymo Open Dataset Challenges 2020
• arxiv: https://arxiv.org/abs/2006.12671

Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness

• intro: Horizon Robotics
• keywords: AFDetV2
• arxiv: https://arxiv.org/abs/2107.14342

1st Place Solution for Waymo Open Dataset Challenge – 3D Detection and Domain Adaptation

• intro: Horizon Robotics
• arxiv: https://arxiv.org/abs/2006.15505

FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

• intro: Samsung AI Center Moscow
• arxiv: https://arxiv.org/abs/2112.00322
• github: https://github.com/samsunglabs/fcaf3d

3D Semantic Segmentation

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/2003.14032
• github: https://github.com/edwardzhou130/PolarSeg

Cloud Transformers

• intro: Samsung AI Center Moscow & Skolkovo Institute of Science and Technology
• arxiv: https://arxiv.org/abs/2007.11679

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

• intro: CUHK & ShanghaiTech University & SenseTime Research
• arxiv: https://arxiv.org/abs/2008.01550
• github: https://github.com/xinge008/Cylinder3D

Projected-point-based Segmentation: A New Paradigm for LiDAR Point Cloud Segmentation

https://arxiv.org/abs/2008.03928

pseudo-LiDAR

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

• intro: CVPR 2019
• project page: https://mileyan.github.io/pseudo_lidar/
• arxiv: https://arxiv.org/abs/1812.07179
• gtihub(official): https://github.com/mileyan/pseudo_lidar

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

• intro: ICLR 2020 Poster
• openreview: https://openreview.net/forum?id=BJedHRVtPB
• github(official): https://github.com/mileyan/Pseudo_Lidar_V2

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/2004.03080
• github: https://github.com/mileyan/pseudo-LiDAR_e2e

Rethinking Pseudo-LiDAR Representation

• intro: ECCV 2020
• arxiv: https://arxiv.org/abs/2008.04582
• github: https://github.com/xinzhuma/patchnet

Demystifying Pseudo-LiDAR for Monocular 3D Object Detection

• intro: University of Trento & Fondazione Bruno Kessler & Facebook
• arxiv: https://arxiv.org/abs/2012.05796

Is Pseudo-Lidar needed for Monocular 3D Object detection?

• intro: ICCV 2021
• intro: Toyota Research Institute
• arxiv: https://arxiv.org/abs/2108.06417

ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

• intro: Beijing Institute of Technology & Baidu Research & National Engineering Laboratory of Deep Learning Technology and Application & University of Macau & University of Technology Sydney
• arxiv: https://arxiv.org/abs/2207.12654
• github: https://github.com/yinjunbo/ProposalContrast

TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection

• intro: Nanyang Technological University & Sensetime Research
• arxiv: https://arxiv.org/abs/2208.03141

Multi-Modal 3D Object Detection

• intro: University of Science and Technology & Harbin Institute of Technology & SenseTime Research & The Chinese University of Hong Kong & IIIS, Tsinghua University AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
• arxiv: https://arxiv.org/abs/2201.06493

3D Detection and Tracking

Joint Monocular 3D Vehicle Detection and Tracking

• intro: ICCV 2019
• project page: https://eborboihuc.github.io/Mono-3DT/
• arxiv: https://arxiv.org/abs/1811.10742
• github(official): https://github.com/ucbdrive/3d-vehicle-tracking

Center-based 3D Object Detection and Tracking

• intro: UT Austin
• intro: 3D Object Detection and Tracking using center points in the bird-eye view.
• arxiv: https://arxiv.org/abs/2006.11275
• github: https://github.com/tianweiy/CenterPoint

3D Object Detection and Tracking Based on Streaming Data

• intro: ICRA 2020
• arxiv: https://arxiv.org/abs/2009.06169

Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

• intro: University of Michigan
• arxiv: https://arxiv.org/abs/2011.02553

Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving

• intro: IROS 2021
• arxiv: https://arxiv.org/abs/2108.04602

PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds

• intro: IROS 2021
• arxiv: https://arxiv.org/abs/2108.06455
• github: https://github.com/shanjiayao/PTT

Real-time 3D Single Object Tracking with Transformer

• intro: IEEE Transactions on Multimedia
• arxiv: https://arxiv.org/abs/2209.00860
• github: https://github.com/shanjiayao/PTT

3D MOT

AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking

• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2012.05894

Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

• intro: Stanford University & Toyota Research Institute
• arxiv: https://arxiv.org/abs/2012.13755

Monocular Quasi-Dense 3D Object Tracking

https://arxiv.org/abs/2103.07351

Lite-FPN for Keypoint-based Monocular 3D Object Detection

• arxiv: https://arxiv.org/abs/2105.00268
• github: https://github.com/yanglei18/Lite-FPN

RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection

• intro: CVPR 2021
• intro: Waymo LLC & Google
• arxiv: https://arxiv.org/abs/2106.13365

VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars

https://arxiv.org/abs/2107.02980

Geometry Uncertainty Projection Network for Monocular 3D Object Detection

• intro: ICCV 2021
• arxiv: https://arxiv.org/abs/2107.13774

Exploring Simple 3D Multi-Object Tracking for Autonomous Driving

• intro: ICCV 2021
• intro: QCraft & Johns Hopkins University
• arxiv: https://arxiv.org/abs/2108.10312

CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion

• intro: Based on this work, we achieved 1st place on the nuScenes tracking leaderboard
• intro: Tsinghua University & Mogo Auto
• arxiv: https://arxiv.org/abs/2209.02540

Transformer

Point Transformer

• intro: Ulm University
• keywords: SortNet
• arxiv: https://arxiv.org/abs/2011.00931

Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Driving

https://arxiv.org/abs/2011.13628

Point Transformer

• intro: University of Oxford & The Chinese University of Hong Kong & Intel Labs
• arxiv: https://arxiv.org/abs/2012.09164

3D Object Detection with Pointformer

• intro: Tsinghua University & BNRist & Alexa AI, Amazon / Columbia University
• arxiv: https://arxiv.org/abs/2012.11409

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

• intro: University of Maryland & Fudan University
• arxiv: https://arxiv.org/abs/2104.11896

Improving 3D Object Detection with Channel-wise Transformer

• intro: ICCV 2021
• intro: Zhejiang University & DAMO Academy, Alibaba Group
• arxiv: https://arxiv.org/abs/2108.10723
• github: https://github.com/hlsheng1/CT3D

TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

• intro CVPR 2022
• intro: Hong Kong University of Science and Technology & Huawei & City University of Hong Kong
• arxiv: https://arxiv.org/abs/2203.11496
• github: https://github.com/XuyangBai/TransFusion/

Projects

OpenLidarPerceptron

• intro: OpenLidarPerceptron is an open source project for LiDAR-based 3D scene perception.
• github: https://github.com/open-mmlab/OpenLidarPerceptron

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds

• github(PyTorch): https://github.com/maudzung/SFA3D

Resources

Awesome-Automanous-3D-Detection-Methods

https://github.com/tyjiang1997/awesome-Automanous-3D-detection-methods