Camera and LiDAR Fusion for vehicle Position Detection using the CARLA Simulator

Vehicle Detection using Data Fusion and Multi-Task Learning. The models are trained using CARLA Simulator to generate Camera (RGB images) and LIDAR point cloud.

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

Extended Kalman Filter and Deep Learning to detect vehicles from RGB and LiDAR data (Sensor Fusion and Tracking project of the Udacity Self-Driving Car Engineer Nanodegree Program)

Ultralytics YOLO11 🚀

Paperlist of awesome 3D detection methods

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

[CVPR2023] LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

This repository is dedicated for 3D detection , sensor fusion and tracking using camera and LIDAR data

Starter Code for the Course 2 project of the Udacity Self-Driving Car Engineer Nanodegree Program

Sensor-Fusion-and-Object-Tracking-2

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation