ICCV2023 paper list 汇总！OCC感知/端到端/V2X/3D检测/分割等
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ICCV2023 paper list
ICCV2023结果陆续都出来了，收到了很多朋友中稿的消息，ICCV 2023今年一共收录 2100多篇，自动驾驶之心也第一时间进行了跟进，将已确定中稿的工作分享给大家，后面将会持续更新！
后面将会按照3D目标检测、BEV、协同感知、语义分割、点云、SLAM、大模型、NeRF、端到端、多模态融合等多个方向罗列！
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1）OCC感知
SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving
Paper：https://arxiv.org/abs/2303.09551
Code：https://github.com/weiyithu/SurroundOcc
OccNet: Scene as Occupancy
Paper：https://arxiv.org/pdf/2306.02851.pdf
Code：https://github.com/OpenDriveLab/OccNet
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
Paper: https://arxiv.org/pdf/2304.05316.pdf
Code: https://github.com/zhangyp15/OccFormer
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Paper: https://arxiv.org/pdf/2303.03991.pdf
Code: https://github.com/JeffWang987/OpenOccupancy
2) 端到端自动驾驶
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Paper: https://arxiv.org/pdf/2303.12077.pdf
Code: https://github.com/hustvl/VAD
DriveAdapter: New Paradigm for End-to-End Autonomous Driving to Alleviate Causal Confusion
Paper: https://arxiv.org/pdf/2308.00398.pdf
Code: https://github.com/OpenDriveLab/DriveAdapter
3）协同感知
Among Us: Adversarially Robust Collaborative Perception by Consensus
Paper: https://arxiv.org/pdf/2303.09495.pdf
Code: https://github.com/coperception/ROBOSAC
HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer
Paper: https://arxiv.org/pdf/2304.10628.pdf
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
待更新！
UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework
Paper: https://arxiv.org/pdf/2303.12400.pdf
ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
Paper: https://arxiv.org/pdf/2307.14187.pdf
Code: https://github.com/KUIS-AI/adapt
4）3D目标检测
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Paper: https://arxiv.org/abs/2206.01256
Code: https://github.com/megvii-research/PETR
StreamPETR: Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
Paper: https://arxiv.org/pdf/2303.11926.pdf
Code: https://github.com/exiawsh/StreamPETR.git
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
Paper: https://arxiv.org/pdf/2301.01283.pdf
Code: https://github.com/junjie18/CMT.git
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection
Paper: https://arxiv.org/abs/2304.13031
Code: https://github.com/AIR-DISCOVER/DQS3D
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
Paper: https://arxiv.org/abs/2304.14340
Code: https://github.com/yichen928/SparseFusion
MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation
Paper: https://arxiv.org/pdf/2304.09801.pdf
Code: https://github.com/ChongjianGE/MetaBEV
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
Paper: https://arxiv.org/pdf/2304.00967.pdf
Code: https://github.com/Sense-X/HoP
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Paper: https://arxiv.org/pdf/2307.07944.pdf
Code: https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet
Learning from Noisy Data for Semi-Supervised 3D Object Detection
Paper: 待更新！
Code: https://github.com/zehuichen123/NoiseDet
SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection
Paper: https://arxiv.org/pdf/2307.11477.pdf
Code: https://github.com/mengtan00/SA-BEV
PG-RCNN: Semantic Surface Point Generation for 3D Object Detection
Paper: https://arxiv.org/pdf/2307.12637.pdf
Code: https://github.com/quotation2520/PG-RCNN
5）语义分割
Rethinking Range View Representation for LiDAR Segmentation
Paper：https://arxiv.org/pdf/2303.05367.pdf
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
已收录，arxiv上暂未放出！
Segment Anything
Paper: https://arxiv.org/abs/2304.02643
Code: https://github.com/facebookresearch/segment-anything
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Paper: https://arxiv.org/abs/2304.09913
Code: https://github.com/shjo-april/MARS
Tube-Link: A Flexible Cross Tube Baseline for Universal Video Segmentation
Paper: https://arxiv.org/pdf/2303.12782.pdf
Code: https://github.com/lxtGH/Tube-Link
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
Paper: https://arxiv.org/pdf/2307.10316.pdf
Code: https://github.com/lizhaoliu-Lec/CPCM
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
Paper: https://arxiv.org/pdf/2307.15063.pdf
Code: https://github.com/MarcBotet/hamlet
PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering
Paper: https://arxiv.org/abs/2304.08965
Code: https://github.com/HalvesChen/PointDC
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
Paper: https://arxiv.org/pdf/2303.05194.pdf
Code: https://github.com/brdav/cma
PODA: Prompt-driven Zero-shot Domain Adaptation
Paper: https://arxiv.org/pdf/2212.03241.pdf
Code: https://github.com/astra-vision/PODA
Similarity Min-Max: Zero-Shot Day-Night Domain Adaptation
Paper: https://red-fairy.github.io/ZeroShotDayNightDA-Webpage/paper.pdf
Code: https://github.com/Red-Fairy/ZeroShotDayNightDA
6）点云感知
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
Paper：https://arxiv.org/pdf/2303.17597.pdf
Code：https://github.com/ldkong1205/Robo3D
Implicit Autoencoder for Point Cloud Self-supervised Representation Learning
Paper: https://arxiv.org/pdf/2201.00785.pdf
Code: https://github.com/SimingYan/IAE
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Paper:
Code: https://github.com/CuiRuikai/Partial2Complete
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
Paper: https://arxiv.org/pdf/2210.01055.pdf
Code: https://github.com/tyhuang0428/CLIP2Point
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
Paper: https://arxiv.org/pdf/2307.08492.pdf
Code: https://github.com/czvvd/SVDFormer
AdaptPoint: Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Paper: 待更新！
Code: https://github.com/Roywangj/AdaptPoint/tree/main
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
Paper: https://arxiv.org/pdf/2303.12384.pdf
Code: https://github.com/IRMVLab/RegFormer
Point Cloud regression with new algebraical representation on ModelNet40 datasets
Paper: 待更新！
Code: https://github.com/flatironinstitute/PointCloud_Regression
Clustering based Point Cloud Representation Learning for 3D Analysis
Paper: https://arxiv.org/pdf/2307.14605.pdf
Code: https://github.com/FengZicai/Cluster3Dseg
Implicit Autoencoder for Point Cloud Self-supervised Representation Learning
Paper: https://arxiv.org/pdf/2201.00785.pdf
Code: https://github.com/SimingYan/IAE
7）目标跟踪
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Paper: https://arxiv.org/pdf/2211.11629.pdf
Code: https://github.com/Jaraxxus-Me/PVT_pp
Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers
Paper: 待更新！
Code: https://github.com/ZHU-Zhiyu/High-Rank_RGB-Event_Tracker
ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object Tracking
Paper: 待更新！
Code: https://github.com/chengche6230/ReST
Multiple Planar Object Tracking
Paper: 待更新！
Code: https://github.com/nku-zhichengzhang/MPOT
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Paper: 待更新！
Code: https://github.com/dsx0511/3DMOTFormer
MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors
Paper: https://arxiv.org/pdf/2303.05071.pdf
Code: https://github.com/slothfulxtx/MBPTrack3D
8) 轨迹预测
EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting
Paper: https://arxiv.org/pdf/2307.09306.pdf
Code: https://github.com/InhwanBae/EigenTrajectory
9）NeRF
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
Paper: https://arxiv.org/abs/2210.00647
Code: https://github.com/zju3dv/IntrinsicNeRF
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
Paper: https://arxiv.org/pdf/2212.02501.pdf
Code: https://github.com/astra-vision/SceneRF
Single-Stage Diffusion NeRF
Paper: https://arxiv.org/abs/2304.06714
Code: https://github.com/Lakonik/SSDNeRF
10）光流
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
Paper: https://arxiv.org/pdf/2303.06209.pdf
Code: https://github.com/duke-vision/semantic-unsup-flow-release
11）双目
ELFNet: Evidential Local-global Fusion for Stereo Matching
Paper: https://arxiv.org/pdf/2308.00728.pdf
Code: https://github.com/jimmy19991222/ELFNet
12）鱼眼
SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning
Paper: 待更新
Code: https://github.com/fh2019ustc/SimFIR
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