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adept

Fast automatic differentiation library in C++

aljabr-1

Methods in Algebra (Volume 1): A Chinese textbook on Algebra

antialiased-cnns

Antialiasing cnns to improve stability and accuracy. In ICML 2019.

apollo

An open autonomous driving platform

asio

Asio C++ Library

bevformer

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

bimocq

Efficient and Conservative Fluids Using Bidirectional Mapping

camera-calibration

ceres-solver

A large scale non-linear optimization library

coma

Convolutional Mesh Autoencoders for Generating 3D Faces

cpp-taskflow

Modern C++ Parallel Task Programming Library

crossscalepatchmatch

Cross-Scale Cost Aggregation for Stereo Matching (CVPR 2014 + TCSVT 2015)

deformable-convolution-v2-pytorch

Deformable ConvNets V2 in PyTorch

dgelom

C++ codes for computer vision and numeric computation.

dive-into-dl-pytorch

Dive into Deep Learning (动手学深度学习) with PyTorch.

dlib

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

easy3d

A lightweight, easy-to-use, and efficient C++ library for processing and rendering 3D data

eigen-git-mirror

Github mirror of the official Eigen's repository (https://bitbucket.org/eigen/eigen)

feature

fima

On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems (TPAMI 2019)

gcc

go-icp

Implementation of the Go-ICP algorithm for globally optimal 3D pointset registration

gradslam

gradslam is an open source differentiable dense SLAM library for PyTorch

icpcuda

Super fast implementation of ICP in CUDA for compute capable devices 2.0 or higher

imgui

Dear ImGui: Bloat-free Graphical User interface for C++ with minimal dependencies

imu_tk

inverse-compositional-stn

Inverse Compositional Spatial Transformer Networks :performing_arts: (CVPR 2017 oral)

inviwo

Inviwo - Interactive Visualization Workshop

ivm_figplot

IVM(R) figure plot with python.

lstm-human-activity-recognition

Human activity recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier