├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Zhuoxiao (Ivan) Chen 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awesome-domain-adaptation-3d-object-detection 2 | [![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT) 3 | 4 | This repo is a collection of AWESOME things about unsupervised domain adaptation (**UDA**), online test-time adaptation (**TTA**), and domain generalization (**DG**) for 3D object detection, including papers, code, etc. Feel free to star and fork. This repo mainly focuses on **AI/CV/ML** conferences and journals. 5 | 6 | ## 2025 7 | + MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection [[ICLR]](https://openreview.net/forum?id=Y6aHdDNQYD) 8 | + Unsupervised 3D Object Detection Domain Adaptation based on Pseudo-label Variance Regularization [[TCSVT]](https://ieeexplore.ieee.org/abstract/document/10870386) 9 | 10 | ## 2024 11 | + DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection [[MM]](https://openreview.net/forum?id=eoaw2A8X4J) [[code]](https://github.com/Jo-wang/DPO) 12 | + CMT: Co-training Mean-Teacher for Unsupervised Domain Adaptation on 3D Object Detection [[MM]](https://openreview.net/forum?id=WhCEsBtJBG) 13 | + Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection [[AAAI]](https://arxiv.org/abs/2406.14878) [[code]](https://github.com/ylwhxht/SRKD-DRET) 14 | + Reg-TTA3D: Better Regression Makes Better Test-Time Adaptive 3D Object Detection [[ECCV]](https://link.springer.com/chapter/10.1007/978-3-031-72775-7_12) 15 | + Domain Generalization of 3D Object Detection by Density-Resampling [[ECCV]](https://link.springer.com/chapter/10.1007/978-3-031-73039-9_26) [[code]](https://github.com/xingyu-group/3D-Density-Resampling-SDG) 16 | + Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection [[CVPR]](https://arxiv.org/abs/2404.19384) [[code]](https://github.com/Zhanwei-Z/PERE) 17 | + Attentive Prototypes for Source-Free Unsupervised Domain Adaptive 3D Object Detection [[WACV]](https://openaccess.thecvf.com/content/WACV2024/html/Hegde_Attentive_Prototypes_for_Source-Free_Unsupervised_Domain_Adaptive_3D_Object_Detection_WACV_2024_paper.html) [[code]](https://github.com/deeptibhegde/AttentivePrototypeSFUDA) 18 | + SOAP: Cross-Sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-Labelling [[WACV]](https://openaccess.thecvf.com/content/WACV2024/html/Huang_SOAP_Cross-Sensor_Domain_Adaptation_for_3D_Object_Detection_Using_Stationary_WACV_2024_paper.html) 19 | + An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains [[CVPR]](https://openaccess.thecvf.com/content/CVPR2024/html/Eskandar_An_Empirical_Study_of_the_Generalization_Ability_of_Lidar_3D_CVPR_2024_paper.html) 20 | 21 | ## 2023 22 | + Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling [[ICCV]](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_Revisiting_Domain-Adaptive_3D_Object_Detection_by_Reliable_Diverse_and_Class-balanced_ICCV_2023_paper.html) [[code]](https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet) 23 | + GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds [[ICCV]](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_Revisiting_Domain-Adaptive_3D_Object_Detection_by_Reliable_Diverse_and_Class-balanced_ICCV_2023_paper.html) [[code]](https://github.com/Liz66666/GPA3D) 24 | + CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection [[AAAI]](https://ojs.aaai.org/index.php/AAAI/article/view/25297) [[code]](https://github.com/4DVLab/CL3D) 25 | + MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection [[TIV]]([https://arxiv.org/abs/2308.05988](https://ieeexplore.ieee.org/abstract/document/10633799)) [[code]](https://github.com/darrenjkt/MS3D) 26 | 27 | 28 | ## 2022 29 | + LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection [[ECCV]](https://link.springer.com/chapter/10.1007/978-3-031-19842-7_11) [[code]](https://github.com/weiyithu/LiDAR-Distillation) 30 | + ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection [[TPAMI]](https://ieeexplore.ieee.org/document/9927350/) [[code]](https://github.com/CVMI-Lab/ST3D) 31 | 32 | ## 2021 33 | + ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection [[CVPR]](https://openaccess.thecvf.com/content/CVPR2021/html/Yang_ST3D_Self-Training_for_Unsupervised_Domain_Adaptation_on_3D_Object_Detection_CVPR_2021_paper.html) [[code]](https://github.com/CVMI-Lab/ST3D) 34 | + SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection [[CVPR]](https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.html) 35 | + Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [[ICCV]](https://openaccess.thecvf.com/content/ICCV2021/html/Luo_Unsupervised_Domain_Adaptive_3D_Detection_With_Multi-Level_Consistency_ICCV_2021_paper.html) [[code]](https://github.com/Jasonkks/mlcnet) 36 | + SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation [[ICCV]](https://openaccess.thecvf.com/content/ICCV2021/html/Xu_SPG_Unsupervised_Domain_Adaptation_for_3D_Object_Detection_via_Semantic_ICCV_2021_paper.html) [[code]](https://github.com/prithusuresh/semantic-point-generation) 37 | + Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training [[NeurIPS]](https://proceedings.neurips.cc/paper_files/paper/2021/hash/b3b25a26a0828ea5d48d8f8aa0d6f9af-Abstract.html) 38 | 39 | ## 2020 40 | + SF-UDA^3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection [[3DV]](https://ieeexplore.ieee.org/abstract/document/9320105) [[code]](https://github.com/saltoricristiano/SF-UDA-3DV) 41 | + Train in Germany, Test in The USA: Making 3D Object Detectors Generalize [[CVPR]](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Train_in_Germany_Test_in_the_USA_Making_3D_Object_CVPR_2020_paper.html) [[code]](https://github.com/cxy1997/3D_adapt_auto_driving) 42 | --------------------------------------------------------------------------------