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3d2gif

convert a 3D format to a gif file and mp4 file

3dhop

3D Heritage Online Presenter

advanced_lane_detection

Advanced lane detection using computer vision

afs

Automatic Feature Selection

anchor3dlane

Official PyTorch implementation for paper`Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection' accepted by CVPR 2023

arsdkbuildutils

awesome_3dreconstruction_list

A curated list of papers & ressources linked to 3D reconstruction from images.

bcn3d-moveo

Open Source 3D Printed Robotic Arm for educational purposes

bow

OpenCV + OpenMP used for Bag of Visual Words implementation

bundler

Structure from Motion (SfM) for Unordered Image Collections

bundler_sfm

Bundler Structure from Motion Toolkit

caffe

Caffe: a fast open framework for deep learning.

camera_simulation

captum

Model interpretability and understanding for PyTorch

ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

ceres-solver

A nonlinear least-squares minimizer

clrnet

Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).

cmvs-pmvs

This software (CMVS) takes the output of a structure-from-motion (SfM) software as input, then decomposes the input images into a set of image clusters of managable size. An MVS software can be used to process each cluster independently and in parallel, where the union of reconstructions from all the clusters should not miss any details that can be otherwise obtained from the whole image set. CMVS should be used in conjunction with an SfM software Bundler and an MVS software PMVS2 (PMVS version 2).

convnext

Code release for ConvNeXt model

crossscalepatchmatch

Cross-Scale Cost Aggregation for Stereo Matching (CVPR 2014 + PatchMatch stereo submit to TPAMI)

cura

Read this, it's important! If you are reading this, then you are looking at the development version of Cura. If you just want to use Cura, take a look at the following location:

cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

cvg_ardrone2_ibvs

We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. urls: http://www.vision4uav.eu/?q=following and http://robotics.asu.edu/ardrone2_ibvs/ .

cvpr2015-opensfm

Source material for the CVPR 2015 Tutorial: Open Source Structure-from-Motion

deep-hough-transform

Jittor and Pytorch code for paper "Deep Hough Transform for Semantic Line Detection" (ECCV 2020, PAMI 2021)

deepmask

Torch implementation of DeepMask and SharpMask

denoising-diffusion-pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

dlib

A toolkit for making real world machine learning and data analysis applications in C++

dsopps_course_project

Project for DSOOPS

eigenlanes

Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022