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awesome-domain-adaptation-3d-object-detection

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A collection of papers about domain adaptation for 3D object detection. Welcome to PR the works (papers, repositories) that are missed by the repo.

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# awesome-domain-adaptation-3d-object-detection
[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)

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.

## 2025
+ MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection [[ICLR]](https://openreview.net/forum?id=Y6aHdDNQYD)
+ Unsupervised 3D Object Detection Domain Adaptation based on Pseudo-label Variance Regularization [[TCSVT]](https://ieeexplore.ieee.org/abstract/document/10870386)

## 2024
+ 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)
+ CMT: Co-training Mean-Teacher for Unsupervised Domain Adaptation on 3D Object Detection [[MM]](https://openreview.net/forum?id=WhCEsBtJBG)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)

## 2023
+ 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)
+ 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)
+ 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)
+ 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)

##  2022
+ 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)
+ 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)

## 2021
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)

## 2020
+ 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)
+ 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)

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