563 Open source projects that are alternatives of or similar to dynamic-occupancy-grid-map

Lane depertaure and Yolo objection detection C++ Linux

Automatic Parking is an autonomous car maneuvering system (part of ADAS) that moves a vehicle from a traffic lane into a parking spot to perform parallel parking. The automatic parking system aims to enhance the comfort and safety of driving in constrained environments where much attention and experience is required to steer the car. The parking…

[TPAMI 2018] Predicting the Driver’s Focus of Attention: the DR(eye)VE Project. A deep neural network learnt to reproduce the human driver focus of attention (FoA) in a variety of real-world driving scenarios.

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GPU-accelerated N-Body particle simulator with visualizer.

This repository consists for gpu bootcamp material for HPC and AI

An ultra-fast, GPU-based large graph embedding algorithm utilizing a novel coarsening algorithm requiring not more than a single GPU.

No description or website provided.

😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

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Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

Code accompanying my blog post on k-means in Python, C++ and CUDA

bazel.cmake mimics the behavior of bazel to simplify the usability of CMake

This is a 'hands-on' tutorial for the RIKEN International School on Data Assimilation (RISDA2018).

FLAME GPU 2 is a GPU accelerated agent based modelling framework for C++ and Python

geometry processing and shape analysis framework

[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

CUDA bindings for Ruby

Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.

Clustering spatial points with algorithm of Fast Search, high performace computing implements of CUDA or parallel in CPU, and runnable implements on python standalone or arcgis.

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JetScan : GPU accelerated portable RGB-D reconstruction system

Docker scripts for building ONNX Runtime with TensorRT and OpenVINO in manylinux environment

This repository contains the solutions to all the exercises for the MOOC about SLAM and PATH-PLANNING algorithms given by professor Claus Brenner at Leibniz University. This repository also contains my personal notes, most of them in PDF format, and many vector graphics created by myself to illustrate the theoretical concepts. Hope you enjoy it! :)

GPU-accelerated Cloth TOP node for TouchDesigner using the NVIDIA Flex physics solver.

This repository contains the code for the CVPR 2020 paper "Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving"

Papers on 3D Object Detection for Autonomous Driving

Several simple examples for popular neural network toolkits calling custom CUDA operators.

GPU Fluid Simulation with Volumetric Rendering

ROS & Gazebo project for 1/10th scale self-driving race cars

continuous energy monte carlo neutron transport in general geometries on GPUs

Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation"

Fork of CUDA GPU memtest 👓

Achieve peak performance on x86 CPUs and NVIDIA GPUs

Particle filtering and sequential parameter inference in Python

2019 DGIST DPoom project under UGRP : SBC and RGB-D camera based full autonomous driving system for mobile robot with indoor SLAM

monolish: MONOlithic LInear equation Solvers for Highly-parallel architecture

The devkit of the nuPlan dataset.

GPU-accelerated data science and visualization in node

Algorithm that converts point cloud data into an occupancy grid

Lightweight Cuda Renderer with Python Wrapper.

Collective Knowledge extension with unified and customizable benchmarks (with extensible JSON meta information) to be easily integrated with customizable and portable Collective Knowledge workflows. You can easily compile and run these benchmarks using different compilers, environments, hardware and OS (Linux, MacOS, Windows, Android). More info:

Experimental parallel compression algorithm

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How to write cuda kernels or c functions in pytorch, especially for former caffe users.

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A stream processing framework for high-throughput applications.

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An open source autonomous driving stack by San Jose State University Autonomous Driving Team