pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

•    Python

Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

Detectron

•    Python

Example output of e2e_mask_rcnn-R-101-FPN_2x using Detectron pretrained weight. Corresponding example output from Detectron.

jetson-inference - Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson

•    C++

Welcome to our training guide for inference and deep vision runtime library for NVIDIA DIGITS and Jetson Xavier/TX1/TX2. This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded platform, improving performance and power efficiency using graph optimizations, kernel fusion, and half-precision FP16 on the Jetson.

albumentations - fast image augmentation library and easy to use wrapper around other libraries

•    Jupyter

You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.

unet - unet for image segmentation

•    Jupyter

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation. The original dataset is from isbi challenge, and I've downloaded it and done the pre-processing.

torch-points3d - Pytorch framework for doing deep learning on point clouds.

•    Python

This is a framework for running common deep learning models for point cloud analysis tasks against classic benchmark. It heavily relies on Pytorch Geometric and Facebook Hydra. The framework allows lean and yet complex model to be built with minimum effort and great reproducibility. It also provide a high level API to democratize deep learning on pointclouds. See our paper at 3DV for an overview of the framework capacities and benchmarks of state-of-the-art networks.

PointCNN - PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

•    Python

Created by Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. See our preprint on arXiv (accepted to NeurIPS 2018) for more details.

Deep-Learning-for-Tracking-and-Detection - Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

I use DavidRM Journal for managing my research data for its excellent hierarchical organization, cross-linking and tagging capabilities. I make available a Journal entry export file that contains tagged and categorized collection of papers, articles and notes about computer vision and deep learning that I have collected over the last few years.

depth_clustering - :taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor

•    C++

This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velodyne sensors, i.e. 16, 32 and 64 beam ones. I recommend using a virtual environment in your catkin workspace (<catkin_ws> in this readme) and will assume that you have it set up throughout this readme. Please update your commands accordingly if needed. I will be using pipenv that you can install with pip.

BlenderProc - A procedural Blender pipeline for photorealistic training image generation

•    Python

A procedural Blender pipeline for photorealistic training image generation. Check out our arXiv paper (we are updating it from time to time) and our workshop paper on sim2real transfer presented at RSS 2020.

awesome-DeepLearning - 深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

•    Jupyter

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

PaddleX - PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

•    Python

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Slicer - Multi-platform, free open source software for visualization and image computing.

•    C++

Multi-platform, free open source software for visualization and image computing.

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

•    Python

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

•    Python

Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.

superpoint_graph - Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

•    Python

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning https://arxiv.org/pdf/1904.02113. To switch to the stable branch with only SPG, switch to release.

cilantro - A lean C++ library for working with point cloud data

•    C++

cilantro is a lean and fast C++ library for working with point cloud data, with emphasis given to the 3D case. It includes efficient implementations for a variety of common operations, providing a clean API and attempting to minimize the amount of boilerplate code. The library is extensively templated, enabling operations on data of arbitrary numerical type and dimensionality (where applicable) and featuring a modular/extensible design of the more complex procedures. At the same time, convenience aliases/wrappers for the most common cases are provided. A high-level description of cilantro can be found in our technical report. Documentation (readthedocs.io, Doxygen API reference) is a work in progress. The short provided examples (built by default) cover a significant part of the library's functionality. Most of them expect a single command-line argument (path to a point cloud file in PLY format). One such input is bundled in examples/test_clouds for quick testing.

Efficient-Segmentation-Networks - Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc

•    Python

This project aims at providing an easy-to-use, modifiable reference implementation for real-time semantic segmentation models using PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, PyTorch v1.1 or later is supported.

lidar-bonnetal - Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving

•    Python

Semantic Segmentation of point clouds using range images. This code provides code to train and deploy Semantic Segmentation of LiDAR scans, using range images as intermediate representation. The training pipeline can be found in /train. We will open-source the deployment pipeline soon.

kagome - Self-contained Japanese Morphological Analyzer written in pure Go

•    Go

Kagome is an open source Japanese morphological analyzer written in pure golang. The MeCab-IPADIC and UniDic (unidic-mecab) dictionary/statiscal models are packaged in Kagome binary. Kagome has segmentation mode for search such as Kuromoji.