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 Kinect Frame Extractor

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

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

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.

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

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

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