#CVPR

Explore a comprehensive collection of CVPR papers ranging from 2017 to 2024, featuring downloads, coding resources, interpretations, and live technical sessions. This page also provides summaries of top papers from 2000 to 2021 and detailed insights into specialized fields like object detection, image processing, and face recognition. Discover valuable links for further resources and stay updated with the latest CVPR 2023 and 2024 publications, offering a deep exploration of the forefront developments in computer vision.

Browse a detailed and continuously updated collection of papers on monocular 3D object detection from 2016 to 2024. This repository showcases the latest methods such as complementary depths and pseudo-labeling frameworks including MonoCD and UniMODE. Access exhaustive methodologies, publication links, and implementations to gain insights into improving detection accuracy in autonomous driving and AI applications. Stay informed on the evolving technologies within the industry.

Access an extensive selection of research papers from CVPR 2024, showcasing the forefront of computer vision and deep learning. The repository offers code implementations to explore advancements in visual intelligence. Stay informed on the latest developments in image synthesis, 3D modeling, and more from top researchers worldwide.

This curated collection offers a detailed overview of significant research papers and code from CVPR conferences held in 2024, 2021, and 2020. It focuses on low-level vision tasks, including but not limited to super-resolution, de-raining, dehazing, deblurring, denoising, image restoration and enhancement, as well as inpainting and interpolation. Targeted at researchers and professionals in the field, the repository presents essential tools and knowledge for advancing image processing techniques. By engaging with the content through actions like starring or contributing, users can help drive collaborative development and progress within the community.

The UniMatch framework provides innovative techniques for semi-supervised semantic segmentation across diverse scenarios such as natural, remote sensing, and medical image contexts. With an official PyTorch implementation, UniMatch delivers robust performance on standard benchmarks including Pascal VOC 2012, Cityscapes, and COCO by utilizing architectures like DeepLabv3+ with ResNet and Xception backbones. It surpasses both traditional supervised and other semi-supervised methods through optimizing confidence thresholds and output stride settings. Discover its effectiveness in remote sensing change detection and medical image segmentation.

HorizonNet implements a novel 1D representation approach for extracting room layouts, developed as a pure Python project. It supports inference of both cuboid and general-shaped room layouts from images, offering features like 3D layout viewing and precise rotation correction. The integration of pano stretch augmentation enhances its adaptability for various applications. HorizonNet includes extensive evaluation metrics, supports custom dataset preparation, and is compatible with multiple datasets, making it a comprehensive solution for 3D room layout analysis.