CVPR2026-Papers-with-Code README

The project's README from github.com/amusi/CVPR2026-Papers-with-Code. As one markdown file: README.md.

CVPR 2026 论文和开源项目合集(Papers with Code)

CVPR 2026 decisions are now available on OpenReview!25.42% = 4090 / 16092

注1:欢迎各位大佬提交issue,分享CVPR 2026论文和开源项目!

注2:关于往年CV顶会论文以及其他优质CV论文和大盘点,详见: https://github.com/amusi/daily-paper-computer-vision

欢迎扫码加入【CVer学术交流群】,可以获取CVPR 2026等最前沿工作!这是最大的计算机视觉AI知识星球!每日更新,第一时间分享最新最前沿的计算机视觉、AIGC、扩散模型、多模态、深度学习、自动驾驶、医疗影像和遥感等方向的学习资料,快加入学起来!

【CVPR 2026 论文开源目录】

3DGS(Gaussian Splatting)

Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting

Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking

FastGS: Training 3D Gaussian Splatting in 100 Seconds

Agent

Avatars

Backbone

CLIP

Mamba

GAN

OCR

NeRF

DETR

Prompt

多模态大语言模型(MLLM)

Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking

UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark

大语言模型(LLM)

具身智能(Embodied AI)

Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI

空间智能(Spatial Intelligence)

Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning

NAS

ReID(重识别)

MOS: Mitigating Optical-SAR Modality Gap for Cross-Modal Ship Re-Identification

扩散模型(Diffusion Models)

Vision Transformer

视觉和语言(Vision-Language)

StructXLIP: Enhancing Vision-language Models with Multimodal Structural Cues

ApET: Approximation-Error Guided Token Compression for Efficient VLMs

Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking

目标检测(Object Detection)

异常检测(Anomaly Detection)

目标跟踪(Object Tracking)

医学图像(Medical Image)

医学图像分割(Medical Image Segmentation)

MedCLIPSeg: Probabilistic Vision–Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation

自动驾驶(Autonomous Driving)

Open-Vocabulary Domain Generalization in Urban-Scene Segmentation

U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences

3D点云(3D-Point-Cloud)

CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation

3D目标检测(3D Object Detection)

3D语义分割(3D Semantic Segmentation)

Low-level Vision

超分辨率(Super-Resolution)

去噪(Denoising)

图像去噪(Image Denoising)

3D人体姿态估计(3D Human Pose Estimation)

#3D Visual Grounding(3D视觉定位)

图像生成(Image Generation)

ExpPortrait: Expressive Portrait Generation via Personalized Representation

视频生成(Video Generation)

图像编辑(Image Editing)

视频编辑(Video Editing)

3D生成(3D Generation)

3D重建(3D Reconstruction)

tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction

Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning

RAP: Fast Feedforward Rendering-Free Attribute-Guided Primitive Importance Score Prediction for Efficient 3D Gaussian Splatting Processing

人体运动生成(Human Motion Generation)

视频理解(Video Understanding)

遥感(Remote)

Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation

知识蒸馏(Knowledge Distillation)

深度估计(Depth Estimation)

立体匹配(Stereo Matching)

暗光图像增强(Low-light Image Enhancement)

图像压缩(Image Compression)](#IC)

视频压缩(Video Compression)](#VC)

UniComp: Rethinking Video Compression Through Informational Uniqueness

场景图生成(Scene Graph Generation)

图像检索(Image Retrieval)

**PinPoint: Evaluation of Composed Image Retrieval with Explicit Negatives, Multi-Image Queries, and Paraphrase Testing **

风格迁移(Style Transfer)

图像质量评价(Image Quality Assessment)

视频质量评价(Video Quality Assessment)

压缩感知(Compressive Sensing)

数据集(Datasets)

其他(Others)

Decoupling Defense Strategies for Robust Image Watermarking

Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery

The Invisible Gorilla Effect in Out-of-distribution Detection

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces

Probing and Bridging Geometry-Interaction Cues for Affordance Reasoning in Vision Foundation Models

GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement

FOZO: Forward-Only Zeroth-Order Prompt Optimization for Test-Time Adaptation

**Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning **

CVPR2026-Papers-with-Code (amusi/CVPR2026-Papers-with-Code)

Searching Database…

这是什么?这是一个经过整理、持续更新的 CVPR 2026 论文及其开源代码实现索引,按计算机视觉主题分类,例如目标检测、图像分割、三维重建、扩散模型、多模态大模型等。
这是数据集、论文,还是阅读清单?它是一个基于 README 的单一索引:一份按研究主题划分的大型目录,每篇论文条目都链接到其 Paper、Code,有时还有 Project page。
这份清单是怎么组织的?我怎样找到想看的内容?条目归在几十个主题标题之下,例如 3DGS、MLLM、Object Detection、Semantic Segmentation、Autonomous Driving 等。进入某个主题章节后,每篇已录用的论文都会列出,并附上论文、代码仓库以及项目主页(如有)的链接。
这里的每篇论文都公开了代码吗?否。许多主题章节仍是空的占位内容,部分列出的论文显示 'Code: None',表示代码尚未发布,或该论文还未加入清单。
这只针对 CVPR 2026 吗?还是也包括其他年份?它专门针对 CVPR 2026,但作者还维护了往年顶级计算机视觉会议的同类清单,例如 ICCV 2025 和 ECCV 2024,链接位于 README 顶部。
开头提到的录用率是多少?README 中写明,CVPR 2026 从 16092 篇投稿中录用了 4090 篇,录用率为 25.42%,数据依据为 OpenReview 的审稿结果。
如果新手只想浏览近期计算机视觉研究趋势,应该从哪里开始?可以直接跳到热门主题章节,例如 Diffusion Models、Multimodal LLMs 或 3D Gaussian Splatting。这些章节都是单一 README 目录下的子标题。
Creator
amusi
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0.5 MB
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Updated
CommitMar 08
Age
6y
Commits · 90 Days