A latent text-to-image diffusion model

Stable Diffusion web UI

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A curated list of awesome Deep Stereo Matching resources

C++ TensorRT implementation of Depth-Anything V1, V2

A formatter for Python files

[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation

[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

Pyramid Stereo Matching Network (CVPR2018)

Linux打包安装最新版 WPS365, 支持一键安装脚本

[ NeurIPS2021] This is an official implementation of our paper "HRFormer: High-Resolution Transformer for Dense Prediction".

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. (ICCV 2021 Oral)

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

[ECCVW 2022] The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

[TPAMI 2025 & CVPR 2023] IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching

Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022

Official MegEngine implementation of CREStereo(CVPR 2022 Oral).

Swin Unet V2 is a modified version of Swin Unet based on Swin Transfomer V2.

[TPAMI 2024 & CVPR 2022] Attention Concatenation Volume for Accurate and Efficient Stereo Matching

[TPAMI 2020] Parallax Attention for Unsupervised Stereo Correspondence Learning

[CVPR 2019] Learning Parallax Attention for Stereo Image Super-Resolution

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Implementation of the Swin Transformer in PyTorch.

TC-Stereo (ECCV2024)

OpenStereo: A Comprehensive Benchmark for Stereo Matching

[ICCV 2025 Highlight] BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment

MonSter++: A Unified Geometric Foundation Model for Stereo and Multi-View Depth Estimation via the Unleashing of Monodepth Priors

【CVPR 2025 Highlight】MonSter: Marry Monodepth to Stereo Unleashes Power

[TPAMI 2025 & CVPR 2023] Iterative Geometry Encoding Volume for Stereo Matching