A multi-sensor capture system for free viewpoint video.

This repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)

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

A ROS package to wrap openvino inference engine and get it working with Myriad and GPU

Prebuilt pyrealsense2 packages for macOSX.

Realsense SDK in python to capture images for measurements

Markerless volumetric alignment for depth sensors. Contains the code of the work "Deep Soft Procrustes for Markerless Volumetric Sensor Alignment" (IEEE VR 2020).

A Docker image equipped with ORB-SLAM 3 and ready for dev. No more GPU, dependencies and build problems !

This project demonstrates real-time object detection using YOLOv8 and opencv with a webcam or Intel RealSense camera.

An Unity Framework for Augmented Reality Sandboxes

An implementation of ORB_SLAM2 in Windows platform with webcam or Intel® RealSense™

Skeleton capture with RealSense and Cubemos

Minimal API for obtaining PCL pointcloud using Intel realsense camera.

Application for capturing Intel Realsense data and outputting rgb/depth image pairs

Simple 3D-Scanner/PCD generator using Intel Realsense L515 camera and rotation matrices

RealSense execution environment built on a Docker container on Ubuntu 20.04. NIVIDA GPU and OpenGL capable. CUADA 11.4.

Unreal Engine 5 Realsense Android and Windows Plugin

DepthProjectionMapping is able to project depth image using intel RealSense D4XX series and a projector. Also, project video by specifying the projection area.

A Project that leading the Robot Arm to pick up the Coca Cola bins, with regards to the Intel Opensource Software and Self-Designed motor to trigger the Open/Close

Project in the context of the IKT-442 class at the University of Agder. Allows to detect movements using a depth camera and a tensorflow model and to propagate inputs to a linux kernel. Our implementation is on a Raspberry pi 4.