Stars
Reference implementations of MLPerf™ inference benchmarks
Implementation and Knowledge Graphs of the ICCV 2023 workshop paper "nuScenes Knowledge Graph - A comprehensive semantic representation of traffic scenes for trajectory prediction"
Generation of diagrams like flowcharts or sequence diagrams from text in a similar manner as markdown
A Vue 3 Component Library. Fairly Complete. Theme Customizable. Uses TypeScript. Fast.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
A Tool for Extracting and Embedding Road Scene-Graphs
Berechnung mehrere Kritikalitätsmetriken zur Bewertung von Szenen und Szenarien.
A curated list of world models for autonomous driving. Keep updated.
Python tools for working with KITTI data.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
[ICCV2023] NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
Creates a semantic scene graph from a traffic scene.
Implementation of the paper "Transformer-based model for monocular visual odometry: a video understanding approach".
Scenarios for testing active safety systems according to Euro NCAP Test Protocols modelled with OpenSCENARIO XML
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing
A minimal Linux-capable 64-bit RISC-V SoC built around CVA6
Optical Flow Estimation using RAFT with PyTorch.
Vehicle Speed Estimation from Video using Deep Learning and Optical Flow in PyTorch.
Estimate the speed of a car given a video from dash cam with optical flow
Download Datasets and Ground Truths: https://github.com/TuSimple/tusimple-benchmark/issues/3
[ECCV 2022 Oral] Perspective Transformer on 3D Lane Detection
Code for the paper entitled "Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection" (CVPR 2021)
Official PyTorch implementation of SegFormer