An opinionated list of Python frameworks, libraries, tools, and resources

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Common used path planning algorithms with animations.

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

PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Papers and Datasets about Point Cloud.

Pytorch framework for doing deep learning on point clouds.

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

[ICLR 2025] From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"

An extension of Open3D to address 3D Machine Learning tasks

(IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"

Financial Data Extraction from Investing.com with Python

🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

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

Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving

(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)

4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

OpenPCSeg: Open Source Point Cloud Segmentation Toolbox and Benchmark

This repository contains utility scripts for the KITTI-360 dataset.

Implementation for PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (CVPR 2020)

Toolbox for constructing alpha shapes.

Common used curves for motion planning.

A LiDAR visualization tool for all your datasets

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

Camera calibration library with spline and parametric distortion models. Maximally powerful, minimally complex.