Contains codes for calculating planes from rgbd-data
-
Updated
Jun 2, 2016 - C++
Contains codes for calculating planes from rgbd-data
Depth-Based Region-of-Interest (ROI) Selection
The purpose of this project is to detect and track people in an indoor environment and recognize events regarding their movement using visual information. The visual information used consists of an RGB stream and a depth stream from an ASUS Xtion Pro or Microsoft Kinect.
Using DepthSense 325 with Python using openCV
Deep-learning approaches to object recognition from 3D data
Patches for Chromium browser to support Intel Senz3d depth and gesture camera
Displays the depth values received by the front-facing camera.
Sample implementation of an application using KinectFusionLib
This repository contains last developments on the detection of the heart rate (HR) by using a kinect.
Capture RGB-D data from a depth camera
Image inpainting of Depth Images using Deep Image Prior
ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (Torch Implementation)
Mapping with Softbank's Pepper platform using fake laser scan and gmapping
ROS-package with some utilities to cope with the noisy and dense data of the kinect and to perform efficiently some useful conversions such as depth image to point cloud or point cloud to laserscan.
ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation)
Nearest neighbor depth completion
Add a description, image, and links to the depth-image topic page so that developers can more easily learn about it.
To associate your repository with the depth-image topic, visit your repo's landing page and select "manage topics."