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2d-and-3d-face-alignment icon 2d-and-3d-face-alignment

This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.

3dmm_cnn icon 3dmm_cnn

Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network

4dface icon 4dface

Real-time 3D face tracking and reconstruction from 2D video

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

bundlefusion icon bundlefusion

[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

cartographer icon cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

cgal icon cgal

The public CGAL repository, see the README below

cvat icon cvat

Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

darknet icon darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

depth-anything icon depth-anything

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

drml icon drml

Deep Region and Multi-label Learning for Facial Action Unit Detection (CVPR16)

eiba icon eiba

Efficient Incremental BA

enft-sfm icon enft-sfm

This source code provides a reference implementation for ENFT-SfM.

face_recognition icon face_recognition

The world's simplest facial recognition api for Python and the command line

graph-cut-ransac icon graph-cut-ransac

The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf

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