OmniKinect

Real-time three-dimensional acquisition of real-world scenes has many important applications in computer graphics, computer vision and human-computer interaction. Inexpensive depth sensors such as the Microsoft Kinect allow to leverage the development of such applications. However, this technology is still relatively recent, and no detailed studies on its scalability to dense and view-independent acquisition have been reported. This paper addresses the question of what can be done with a larger number of Kinects used simultaneously. We describe an interference-reducing physical setup, a calibration procedure and an extension to the KinectFusion algorithm, which allows to produce high quality volumetric reconstructions from multiple Kinects whilst overcoming systematic errors in the depth measurements. We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion. Our system provides practical insight into achievable spatial and radial range and into bandwidth requirements for depth data acquisition. Finally, we present a number of practical applications of our system.

2011

We analyze Kinect as a 3D measuring device, experimentally investigate depth measurement resolution and error properties and make a quantitative comparison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose Kinect geometrical model and its calibration procedure providing an accurate calibration of Kinect 3D measurement and Kinect cameras. We demonstrate the functionality of Kinect calibration by integrating it into an SfM pipeline where 3D measurements from a moving Kinect are transformed into a common coordinate system by computing relative poses from matches in color camera.

2012

This paper proposes a novel method to reconstruct dynamic scenes by integrating depth data obtained by multiple Kinects, which cannot be synchronized to one another. In this method, the multiple Kinects located so as to cover the whole surface are firstly calibrated so that their depth data are mapped into the world coordinate system. The synchronous depth data for each Kinect is then generated by interpolation of temporally neighboring captured data. Experimental results of marching person reconstruction show the effectiveness of our method.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011

This paper presents an investigation of the geometric quality of depth data obtained by the Kinect sensor. Based on the mathematical model of depth measurement by the sensor a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimetres up to about 4 cm at the maximum range of the sensor. The accuracy of the data is also found to be influenced by the low resolution of the depth measurements.

Sensors, 2012

Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings, 2012

This paper presents an analysis and experimental study of the Kinect's depth sensor. Particular attention is given to resolution, quantization error and random distribution of depth data. The effects of color and reflectance characteristics of the object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version.

2011

Abstract KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. <br><br> For this purpose, a 3D model of a DUPLO<sup>TM</sup> bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR ...

RGB-D cameras like the Microsoft Kinect had a huge impact on recent research in Computer Vision as well as Robotics. With the release of the Kinect v2 a new promising device is available, which will -most probably -be used in many future research. In this paper, we present a systematic comparison of the Kinect v1 and Kinect v2. We investigate the accuracy and precision of the devices for their usage in the context of 3D reconstruction, SLAM or visual odometry. For each device we rigorously figure out and quantify influencing factors on the depth images like temperature, the distance of the camera or the scene color. Furthermore, we demonstrate errors like flying pixels and multipath interference. Our insights build the basis for incorporating or modeling the errors of the devices in follow-up algorithms for diverse applications.

Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.