Give me a star please. This document contains instructions/notes on how to install the Azure Kinect camera. Here I collected experiences that have arisen during the development of the following software for Azure Kinect DK camera: AK_ACQS Azure Kinect Acquisition System AK_SM_RECORDER - Azure Kinect Standalone Mode AK_FRAEX - Azure Kin

Final project titled "Point Cloud Segmentation and Object Tracking using RGB-D Data" for the Machine Vision (EE 576) course.

AK_VIDEO_ANALYZER that analyses videos on which to automatically detect apples, estimate their size and predict yield at the plot or per hectare scale using the appropriate simulated algorithms.

Images from an RGB-D camera are used to detect/classify objects in 2D, then detections are projected on the 3D point cloud.

AKFruitData - AK_FRAEX: Tool for extracting frames from video files produced with Azure Kinect cameras. RGB-D camera, Data acquisition, Data extraction, Fruit yield trials, Precision fruticulture.https://doi.org/10.1016/j.softx.2022.101231

Mapping of ROS environment with a RGB-D camera mounted on a robot

AKFruitData - AK_SM_RECORDER. Azure Kinect single mode recorder. https://pypi.org/project/ak-sm-recorder/

implementation of Unsupervised single image depth prediction with CNNs

AKFruitYield: AK_SW_BENCHMARKER Azure Kinect Size Estimation & Weight Prediction Benchmarker.

AKFruitData - ak_acquisition_system is a software solution for data acquisition in fruit orchards using a sensor system boarded on a terrestrial vehicle. It allows the coordination of computers and sensors through the sending of remote commands via a GUI. https://doi.org/10.1016/j.softx.2022.101231

This is an unofficial Python demo of the Self-Supervised Label Generator (SSLG), presented in "Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs. Our SSLG can be used effectively for self-supervised drivable area and road anomaly segmentation based on RGB-D data".

AKFruitData. This repository contents source code of two applications presented in the article https://doi.org/10.1016/j.softx.2022.101231 at the time of its publication in SoftwareX Journal.

Unsupervised Domain Adaptation through Inter-modal Rotation and Jigsaw Puzzle assembly for RGB-D Object Recognition