CVPR2026-Papers-with-Code

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CVPR2026-Papers-with-Code

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CVPR 2026 논 및 오픈소스 프로젝트 모음

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# CVPR 2026 논문 및 오픈소스 프로젝트 모음 (Papers with Code)
CVPR 2026 심사 결과가 이제 OpenReview에 공개되었습니다! 25.42% = 4090 / 16092
> 노트 1: 기여어(issue)를 통해 CVPR 2026 논문과 오픈소스 프로젝트를 공유해 주신 분들 환영합니다!
>
> 노트 2: 역대 CV 주요 학회 논문 및 기타 우수한 CV 논문과 총정리에 대한 자세한 내용은 다음을 참조하세요: https://github.com/amusi/daily-paper-computer-vision
>
> - [ICCV 2025](https://github.com/amusi/ICCV2025-Papers-with-Code)
> - [ECCV 2024](https://github.com/amusi/ECCV2024-Papers-with-Code)
QR 코드를 스캔하여 [CVer 학술 교류 그룹(CVer学术交流群)]에 가입하고 CVPR 2026을 비롯한 최첨단 연구 결과들을 확인해 보세요! 이곳은 가장 큰 컴퓨터 비전 AI 지식星球입니다! 매일 업데이트되며 컴퓨터 비전, AIGC, 확산 모델(Diffusion Models), 멀티모달, 딥러닝, 자율주행, 의료 영상, 원격 탐사 등의 최신 및 최첨단 학습 자료를 실시간으로 공유합니다. 지금 바로 가입하고 학습을 시작하세요!
![](https://raw.githubusercontent.com/amusi/CVPR2026-Papers-with-Code/main/CVer学术交流群.png)
# 【CVPR 2026 논문 오픈소스 디렉토리】
- [3DGS(Gaussian Splatting)](#3DGS)
- [Agent](#Agent)
- [Avatars](#Avatars)
- [Backbone](#Backbone)
- [CLIP](#CLIP)
- [Mamba](#Mamba)
- [Embodied AI](#Embodied-AI)
- [GAN](#GAN)
- [GNN](#GNN)
- [멀티모달 대형 언어 모델(MLLM)](#MLLM)
- [대형 언어 모델(LLM)](#LLM)
- [구현 지능(Embodied AI)](#Embodied)
- [공간 지능(Spatial Intelligence)](#SI)
- [NAS](#NAS)
- [OCR](#OCR)
- [NeRF](#NeRF)
- [DETR](#DETR)
- [확산 모델(Diffusion Models)](#Diffusion)
- [ReID (재식별)](#ReID)
- [롱테일 분포(Long-Tail)](#Long-Tail)
- [Vision Transformer](#Vision-Transformer)
- [비전 및 언어(Vision-Language)](#VL)
- [자기 지도 학습(Self-supervised Learning)](#SSL)
- [데이터 증강(Data Augmentation)](#DA)
- [객체 검출(Object Detection)](#Object-Detection)
- [이상 검출(Anomaly Detection)](#Anomaly-Detection)
- [비주얼 트래킹(Visual Tracking)](#VT)
- [시맨틱 세그멘테이션(Semantic Segmentation)](#Semantic-Segmentation)
- [인스턴스 세그멘테이션(Instance Segmentation)](#Instance-Segmentation)
- [파노픽 세그멘테이션(Panoptic Segmentation)](#Panoptic-Segmentation)
- [의료 영상(Medical Image)](#MI)
- [의료 영상 세그멘테이션(Medical Image Segmentation)](#MIS)
- [비디오 객체 세그멘테이션(Video Object Segmentation)](#VOS)
- [비디오 인스턴스 세그멘테이션(Video Instance Segmentation)](#VIS)
- [참조 이미지 세그멘테이션(Referring Image Segmentation)](#RIS)
- [이미지 매팅(Image Matting)](#Matting)
- [이미지 편집(Image Editing)](#Image-Editing)
- [Low-level Vision](#LLV)
- [초해상도(Super-Resolution)](#SR)
- [디노이징(Denoising)](#Denoising)
- [디블러링(Deblur)](#Deblur)
- [자율주행(Autonomous Driving)](#Autonomous-Driving)
- [3D 포인트 클라우드(3D Point Cloud)](#3D-Point-Cloud)
- [3D 객체 검출(3D Object Detection)](#3DOD)
- [3D 시맨틱 세그멘테이션(3D Semantic Segmentation)](#3DSS)
- [3D 객체 트래킹(3D Object Tracking)](#3D-Object-Tracking)
- [3D 시맨틱 씬 컴완(3D Semantic Scene Completion)](#3DSSC)
- [3D 레지스트레이션(3D Registration)](#3D-Registration)
- [3D 인체 포즈 추정(3D Human Pose Estimation)](#3D-Human-Pose-Estimation)
- [3D 인체 메시 추정(3D Human Mesh Estimation)](#3D-Human-Pose-Estimation)
- [3D Visual Grounding (3D 시각적 그라운딩)](#3DVG)
- [의료 영상(Medical Image)](#Medical-Image)
- [이미지 생성(Image Generation)](#Image-Generation)
- [비디오 생성(Video Generation)](#Video-Generation)
- [3D 생성(3D Generation)](#3D-Generation)
- [비디오 이해(Video Understanding)](#Video-Understanding)
- [행동 검출(Action Detection)](#Action-Detection)
- [원격 탐사(Remote)](#Remote)
- [텍스트 검출(Text Detection)](#Text-Detection)
- [지식 증류(Knowledge Distillation)](#KD)
- [모델 프루닝(Model Pruning)](#Pruning)
- [이미지 압축(Image Compression)](#IC)
- [비디오 압축(Video Compression)](#VC)
- [3차원 재구성(3D Reconstruction)](#3D-Reconstruction)
- [깊이 추정(Depth Estimation)](#Depth-Estimation)
- [궤적 예측(Trajectory Prediction)](#TP)
- [차선 검출(Lane Detection)](#Lane-Detection)
- [이미지 캡셔닝(Image Captioning)](#Image-Captioning)
- [시각적 질의응답(Visual Question Answering)](#VQA)
- [수화 인식(Sign Language Recognition)](#SLR)
- [비디오 예측(Video Prediction)](#Video-Prediction)
- [신시점 합성(Novel View Synthesis)](#NVS)
- [Zero-Shot Learning (제로 샷 학습)](#ZSL)
- [스테레오 매칭(Stereo Matching)](#Stereo-Matching)
- [특징 매칭(Feature Matching)](#Feature-Matching)
- [저조도 이미지 향상(Low-light Image Enhancement)](#Low-light)
- [씬 그래프 생성(Scene Graph Generation)](#SGG)
- [이미지 검색(Image Retrieval)](#Image-Retrieval)
- [스타일 전이(Style Transfer)](#ST)
- [암시적 신경 표현(Implicit Neural Representations)](#INR)
- [이미지 품질 평가(Image Quality Assessment)](#IQA)
- [비디오 품질 평가(Video Quality Assessment)](#Video-Quality-Assessment)
- [압축 센싱(Compressive Sensing)](#CS)
- [데이터셋(Datasets)](#Datasets)
- [신규 태스크(New Tasks)](#New-Tasks)
- [기타(Others)](#Others)
# 3DGS(Gaussian Splatting)
\*\*Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting\*\*
- Paper: https://arxiv.org/abs/2602.20933
- C

Code:
- Project: https://sk-fun.fun/DropAnSH-GS
\*\*Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking\*\*
- Paper: https://arxiv.org/abs/2512.01329
- Project: https://haza628.github.io/tagSplat/
\*\*FastGS: Training 3D Gaussian Splatting in 100 Seconds\*\*
- Paper: https://arxiv.org/pdf/2511.04283
- Code: https://github.com/fastgs/FastGS
- Project: https://fastgs.github.io/
# 에이전트 (Agent)
# 아바타 (Avatars)
# 백본 (Backbone)
# CLIP
# Mamba
# GAN
# OCR
# NeRF
# DETR
# 프롬프트 (Prompt)
# 멀티모달 대형 언어 모델 (MLLM)
\*\*Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking\*\*
- Paper: https://arxiv.org/abs/2602.20330
- Code: https://github.com/UIUC-MONET/vlm-circuit-tracing
\*\*UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark\*\*
- Paper: https://arxiv.org/abs/2603.05075
- Code:
- Project: https://any2any-mllm.github.io/unim/
# 대형 언어 모델 (LLM)
# 체화된 인공지능 (Embodied AI)
\*\*Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI\*\*
- Paper: https://arxiv.org/abs/2511.20620
- Code: https://github.com/ai4ce/wanderland
- Project: https://ai4ce.github.io/wanderland/
# 공간 지능 (Spatial Intelligence)
\*\*Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning\*\*
- Paper: https://arxiv.org/abs/2510.27606
- Code: https://github.com/InternLM/Spatial-SSRL
- Model: https://huggingface.co/internlm/Spatial-SSRL-7B
# NAS
# ReID (재식별)
\*\*MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification\*\*
- Paper: https://arxiv.org/abs/2512.03404
- Code: https://github.com/yjzhao1019/MOS
# 확산 모델 (Diffusion Models)
# Vision Transformer
# 비전 및 언어 (Vision-Language)
\*\*StructXLIP: Enhancing Vision-language Models with Multimodal Structural Cues\*\*
- Paper: https://arxiv.org/abs/2602.20089
- Code: https://github.com/intelligolabs/StructXLIP
\*\*ApET: Approximation-Error Guided Token Compression for Efficient VLMs\*\*
- Paper: https://arxiv.org/abs/2602.19870
- Code: https://github.com/MaQianKun0/ApET
\*\*Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking\*\*
- Paper: https://arxiv.org/abs/2602.20330
- Code: https://github.com/UIUC-MONET/vlm-circuit-tracing
# 객체 검출 (Object Detection)
# 이상 검출 (Anomaly Detection)
# 객체 추적 (Object Tracking)
# 의료 영상 (Medical Image)
# 의료 영상 분할 (Medical Image Segmentation)
\*\*MedCLIPSeg: Probabilistic Vision–Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation\*\*
- Paper: https://arxiv.org/abs/2602.20423
- Code: https://github.com/HealthX-Lab/MedCLIPSeg
- Project: https://tahakoleilat.github.io/MedCLIPSeg
# 자율주행 (Autonomous Driving)
\*\*Open-Vocabulary Domain Generalization in Urban-Scene Segmentation\*\*
- Paper: https://arxiv.org/pdf/2602.18853
- Code: https://github.com/DZhaoXd/s2\_corr
\*\*U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences\*\*
- Paper: https://arxiv.org/abs/2512.02982
- Code: https://github.com/worldbench/U4D
# 3D 포인트 클라우드 (3D-Point-Cloud)
\*\*CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation\*\*
- Paper: https://arxiv.org/abs/2602.20409
- Code: https://github.com/SarthakM320/CLIPoint3D
# 3D 객체 검출 (3D Object Detection)
# 3D 의미 분할 (3D Semantic Segmentation)
# 로우레벨 비전 (Low-level Vision)
# 초해상도 (Super-Resolution)
# 노이즈 제거 (Denoising)
## 이미지 노이즈 제거 (Image Denoising)
# 3D 인간 자세 추정 (3D Human Pose Estimation)
# 3D Visual Grounding (3D 시각적 접지)
# 이미지 생성 (Image Generation)
ExpPortrait: Expressive Portrait Generation via Personalized Representation
- Paper: https://arxiv.org/abs/2602.19900
- Code:
# 비디오 생성 (Video Generation)
# 이미지 편집 (Image Editing)
# 비디오 편집 (Video Editing)
# 3D 생성 (3D Generation)
# 3D 재구성 (3D Reconstruction)
\*\*tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction\*\*
- Project: https://cwchenwang.github.io/tttLRM/
- Paper: https://arxiv.org/abs/2602.20160
- Code: https://github.com/cwchenwang/tttLRM
\*\*Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning\*\*
- Project: https://flow3r-project.github.io/
- Paper

r: https://arxiv.org/abs/2602.20157
- Code: https://github.com/Kidrauh/flow3r
\*\*RAP: Fast Feedforward Rendering-Free Attribute-Guided Primitive Importance Score Prediction for Efficient 3D Gaussian Splatting Processing\*\*
- Paper: https://arxiv.org/abs/2602.19753
- Code: https://github.com/yyyykf/RAP
# 휴먼 모션 생성 (Human Motion Generation)
# 비디오 이해 (Video Understanding)
# 원격 탐사 (Remote Sensing)
Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation
- Paper: https://arxiv.org/abs/2602.19863
- Code: None
# 지식 증류 (Knowledge Distillation)
# 깊이 추정 (Depth Estimation)
# 스테레오 매칭 (Stereo Matching)
# 저조도 이미지 향상 (Low-light Image Enhancement)
# 이미지 압축 (Image Compression)](#IC)
# 비디오 압축 (Video Compression)](#VC)
\*\*UniComp: Rethinking Video Compression Through Informational Uniqueness\*\*
- Paper: https://arxiv.org/abs/2512.03575
- Code: https://github.com/TimeMarker-LLM/UniComp
# 장면 그래프 생성 (Scene Graph Generation)
# 이미지 검색 (Image Retrieval)
\*\*PinPoint: Evaluation of Composed Image Retrieval with Explicit Negatives, Multi-Image Queries, and Paraphrase Testing
\*\*
- Paper: https://arxiv.org/abs/2603.04598
- Code:
# 스타일 전송 (Style Transfer)
# 이미지 품질 평가 (Image Quality Assessment)
# 비디오 품질 평가 (Video Quality Assessment)
# 압축 센싱 (Compressive Sensing)
# 데이터셋 (Datasets)
# 기타 (Others)
\*\*Decoupling Defense Strategies for Robust Image Watermarking\*\*
- Paper: https://arxiv.org/abs/2602.20053
- Code: None
\*\*Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery\*\*
- Paper: https://arxiv.org/abs/2602.19910
- Code:
\*\*The Invisible Gorilla Effect in Out-of-distribution Detection\*\*
- Paper: https://arxiv.org/abs/2602.20068
- Code: https://github.com/HarryAnthony/Invisible\_Gorilla\_Effect
\*\*SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images\*\*
- Paper: https://arxiv.org/abs/2602.20412
- Code:
\*\*RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces\*\*
- Paper: https://arxiv.org/abs/2602.20618
- Code:
\*\*Probing and Bridging Geometry-Interaction Cues for Affordance Reasoning in Vision Foundation Models\*\*
- Paper:
- Code:
\*\*GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement\*\*
- Paper: https://arxiv.org/abs/2603.05095
- Code:
\*\*FOZO: Forward-Only Zeroth-Order Prompt Optimization for Test-Time Adaptation\*\*
- Paper: https://arxiv.org/abs/2603.04733
- Code: https://github.com/eVI-group-SCU/FOZO
\*\*Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
\*\*
- Paper: https://arxiv.org/abs/2603.04825
- Code: https://github.com/RyanZhaoIc/CAD

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