Awesome-Multi-Modal-3D-Detection

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Awesome-Multi-Modal-3D-Detection

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Awesome Multi-Modal 3D Detection

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# Awesome Multi-Modal 3D Detection [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/IDEACVR/awesome-detection-transformer)

This is a collecttion of papers for LiDAR-camera fusion 3D detection methods.

We classify papers into three groups based on their fusion types: Early Fusion、Intermediate Fusion、Late Fusion.

If you find some overlooked papers, please open issues or pull requests. :star::star::star:

# Contents
- [Early Fusion](#early-fusion)
- [Intermediate Fusion](#intermediate-fusion)
  - [2024](#2024)
  - [2023](#2023)
  - [2022](#2022)
  - [2021](#2021)
  - [2020](#2020)
  - [2019](#2019)
  - [2018](#2018)
- [Late Fusion](#late-fusion)

# Early Fusion

PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection

 
ECCVW 2022.
<a href='https://arxiv.org/pdf/2211.08055'>[paper]</a>

Multimodal Virtual Point 3D Detection 

 
NeurIPS 2021.
<a href='https://arxiv.org/pdf/2111.06881'>[paper]</a> <a href='https://github.com/tianweiy/MVP'>[code]</a>

PointAugmenting: Cross-Modal Augmentation for 3D Object Detection

 
CVPR 2021.
<a href='https://ieeexplore.ieee.org/document/9578812'>[paper]</a> <a href='https://github.com/VISION-SJTU/PointAugmenting'>[code]</a>

Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR

 
ICCVW 2021.
<a href='https://openaccess.thecvf.com/content/ICCV2021W/AVVision/papers/Paigwar_Frustum-PointPillars_A_Multi-Stage_Approach_for_3D_Object_Detection_Using_RGB_ICCVW_2021_paper.pdf'>[paper]</a> <a href='https://github.com/anshulpaigwar/Frustum-Pointpillars'>[code]</a>

FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection

 
ITSC 2021.
<a href='https://arxiv.org/abs/2106.12449'>[paper]</a>

PointPainting: Sequential Fusion for 3D Object Detection

 
CVPR 2020.
<a href='https://arxiv.org/pdf/1911.10150'>[paper]</a> <a href='https://github.com/Song-Jingyu/PointPainting'>[code]</a>

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

 
CVPRW 2019.
<a href='https://arxiv.org/pdf/1904.07537'>[paper]</a> <a href='https://github.com/maudzung/Complex-YOLOv4-Pytorch'>[code]</a>

Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection

 
IROS 2019.
<a href='https://arxiv.org/pdf/1903.01864'>[paper]</a> <a href='https://github.com/Gorilla-Lab-SCUT/frustum-convnet'>[code]</a>

RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

 
IV 2019.
<a href='https://arxiv.org/pdf/1811.03818'>[paper]</a> <a href='https://github.com/collector-m/RoarNet'>[code]</a>

Frustum PointNets for 3D Object Detection from RGB-D Data

 
CVPR 2018.
<a href='https://arxiv.org/pdf/1711.08488'>[paper]</a> <a href='https://github.com/charlesq34/frustum-pointnets'>[code]</a>

# Intermediate Fusion
## 2024

IS-FUSION: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

 
CVPR 2024.
<a href='https://arxiv.org/pdf/2403.15241'>[paper]</a> <a href='https://github.com/yinjunbo/IS-Fusion'>[code]</a>

Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection

 
ECCV 2024.
<a href='https://arxiv.org/pdf/2311.07152'>[paper]</a> <a href='https://github.com/HuangJunJie2017/BEVDet'>[code]</a>

Lift-Attend-Splat: Bird’s-eye-view camera-lidar fusion using transformers

 
CVPRW 2024.
<a href='https://arxiv.org/pdf/2312.14919'>[paper]</a> <a href=''>[code]</a>

## 2023

MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

 
CVPR 2023.
<a href='https://arxiv.org/pdf/2209.03102'>[paper]</a> <a href='https://github.com/SxJyJay/MSMDFusion'>[code]</a>

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

 
ICCV 2023.
<a href='https://arxiv.org/pdf/2308.07732'>[paper]</a> <a href='https://github.com/Haiyang-W/UniTR'>[code]</a>

Cross Modal Transformer: Towards Fast and Robust 3D Object Detection

 
ICCV 2023.
<a href='https://arxiv.org/pdf/2301.01283'>[paper]</a> <a href='https://github.com/junjie18/CMT'>[code]</a>

SemanticBEVFusion: Rethinking LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection

 
IROS 2023.
<a href='https://arxiv.org/pdf/2212.04675'>[paper]</a>

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

 
ICRA 2023.
<a href='https://arxiv.org/pdf/2205.13542'>[paper]</a> <a href='https://github.com/mit-han-lab/bevfusion'>[code]</a>

Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects

 
ICRA 2023.
<a href='https://arxiv.org/pdf/2209.12880'>[paper]</a>

## 2022

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

 
CVPR 2022.
<a href='https://arxiv.org/pdf/2203.11496'>[paper]</a> <a href='https://github.com/XuyangBai/TransFusion'>[code]</a>

DeepInteraction: 3D Object Detection via Modality Interaction

 
NeurIPS 2022.
<a href='https://arxiv.org/pdf/2208.11112'>[paper]</a> <a href='https://github.com/fudan-zvg/DeepInteraction'>[code]</a>

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

 
NeurIPS 2022.
<a href='https://arxiv.org/pdf/2206.00630'>[paper]</a> <a href='https://github.com/dvlab-research/UVTR'>[code]</a>

AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection

 
ECCV 2022.
<a href='https://arxiv.org/abs/2207.10316'>[paper]</a> <a href='https://github.com/zehuichen123/AutoAlignV2'>[code]</a>

AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

 
IJCAI 2022.
<a href='https://arxiv.org/abs/2201.06493'>[paper]</a>

## 2021

Multi-Stage Fusion for Multi-Class 3D Lidar Detection

 
ICCVW 2021.
<a href='https://openaccess.thecvf.com/content/ICCV2021W/AVVision/papers/Wang_Multi-Stage_Fusion_for_Multi-Class_3D_Lidar_Detection_ICCVW_2021_paper.pdf'>[paper]</a>

## 2020

3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection

 
ECCV 2020.
<a href='https://arxiv.org/abs/2004.12636'>[paper]</a> <a href='https://github.com/rasd3/3D-CVF'>[code]</a>

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

 
ECCV 2020.
<a href='https://arxiv.org/abs/2007.08856'>[paper]</a> <a href='https://github.com/happinesslz/EPNet'>[code]</a>

## 2019

Multi-Task Multi-Sensor Fusion for 3D Object Detection

 
CVPR 2019.
<a href='https://arxiv.org/abs/2012.12397'>[paper]</a> <a href='https://github.com/abcSup/NotEnoughSleepAI'>[code]</a>

MVX-Net: Multimodal VoxelNet for 3D Object Detection

 
ICRA 2019.
<a href='https://arxiv.org/abs/1904.01649'>[paper]</a>

## 2018

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

 
CVPR 2018.
<a href='https://arxiv.org/abs/1711.10871'>[paper]</a> <a href='https://github.com/JuliaChae/Pointfusion'>[code]</a>

Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection

 
IV 2018.
<a href='https://arxiv.org/abs/1711.06703'>[paper]</a> <a href='https://github.com/ZiningWang/Sparse_Pooling'>[code]</a>

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

 
ECCV 2018.
<a href='http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf'>[paper]</a>

# Late Fusion

SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection

 
ICCV 2023.
<a href='https://arxiv.org/pdf/2304.14340'>[paper]</a> <a href='https://github.com/yichen928/SparseFusion'>[code]</a>

Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection

 
WACV 2022.
<a href='https://ieeexplore.ieee.org/document/9706631'>[paper]</a>

Cross-Modality 3D Object Detection

 
WACV 2021.
<a href='https://ieeexplore.ieee.org/document/9423069'>[paper]</a> <a href='https://github.com/VISION-SJTU/CM3DDet'>[code]</a>

CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

 
IROS 2020.
<a href='https://arxiv.org/pdf/2009.00784'>[paper]</a> <a href='https://github.com/pangsu0613/CLOCs'>[code]</a>

Multi-View 3D Object Detection Network for Autonomous Driving

 
CVPR 2017.
<a href='https://arxiv.org/pdf/1611.07759'>[paper]</a> <a href='https://github.com/bostondiditeam/MV3D'>[code]</a>

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