In this paper, we estimate the accuracy of 3D object reconstruction using multiple Kinect sensors. First, we discuss the calibration of multiple Kinect sensors, and provide an analysis of the accuracy and resolution of the depth data. Next, the precision of coordinate mapping between sensors data for registration of depth and color images is evaluated. We test a proposed system for 3D object reconstruction with four Kinect V2 sensors and present reconstruction accuracy results. Ex-periments and computer simulation are carried out using Matlab and Kinect V2.

Fusion of information from multiple Kinect sensors for 3D object reconstruction

Three-dimensional (3D) imaging has recently been applied to human gesture recognition using depth maps from RGB-D sensors. An alternative which has been scarcely explored is 3D Integral Imaging, which has shown to give very competitive results in object reconstruction and recognition tasks, even under challenging conditions (e.g. low illumination, occlusions). Integral Imaging has some remarkable advantages over other sensors that may give 3D information (like RGB-D sensors). One of the most important ones is its long range working capability, which stands out even more when compared against other sensors that lose their capabilities for depths of 2m or more. In this paper we present results corresponding to the application of the integral imaging 3D acquisition technique for the recognition of human gestures, when there are occlusions that may hinder the recognition capability. We also present results comparing its capability against that given by an RGB-D sensor (Kinect) and that obtained when only one of the cameras in the camera array is used. Our results show that Integral Imaging compares more or less similarly to Kinect and the monocular case when there are not occlusions, but much more favorably when there are. We also show that the camera spatial resolution may be an issue to account for, when we refer to gesture recognition under occlusions, for the monocular case, but it is less sensitive for the Integral Imaging case, because the features that are extracted from Integral Imaging seem to be more descriptive and discriminative than for the monocular counterpart case.

/pdf/three-dimensional-integral-imaging-for-gesture-recognition-egnmz0tpyp.pdf

Three-dimensional integral imaging for gesture recognition under occlusions

We report a new procedure for the capture and processing of light proceeding from 3D scenes of some cubic meters in size. Specifically we demonstrate that with the information provided by a kinect device it is possible to generate an array of microimages ready for their projection onto an integral-imaging monitor. We illustrate our proposal with some imaging experiment in which the final result are 3D images displayed with full parallax.

Towards 3D Television Through Fusion of Kinect and Integral-Imaging Concepts

In this paper, we propose a new algorithm for dense 3D object reconstruction using a RGB-D sensor at high rate. In order to obtain a dense shape recovery of a 3D object, an efficient merging of the current and incoming point clouds obtained with the Iterative Closest Point is suggested. As a result, incoming frames are aligned to the dense 3D model. The accuracy of the proposed 3D object reconstruction algorithm on real data is compared to that of the estate-of-the-art reconstruction algorithms.

Real-time dense 3D object reconstruction using RGB-D sensor

Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user’s hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual Point-Net architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.

Duodepth: Static Gesture Recognition Via Dual Depth Sensors

In this paper, we propose a new method for the occluded object visualization using multiple Kinect sensors. The quality of occluded object reconstructed from conventional reconstruction method with elemental images captured by common camera arrays in integral imaging is usually degraded due to the existence of occlusion object in the 3D space which is a common case in reality. Even though some occlusion removal algorithms were proposed to improve the resolution of reconstructed occluded object, all of them are very time-consuming and make the 3D object reconstruction process inefficient. On the contrary, the Kinect sensors not only provide RGB images but also depth images. Since the depth and RGB color image is captured by two different cameras on Kinect sensor at different location, the depth image should be mapped to the color image. After image mapping (or registration), the same pixel location on depth and RGB color image would represent the same 3D space point. As result, the depth image after mapping can be used to remove the occlusion object in the corresponding RGB image (or elemental image) before doing object reconstruction in integral imaging. Consequently, the occluded object can be visualized with a high quality and will not take a long time as other methods.

Partially occluded object reconstruction using multiple Kinect sensors

This paper provides an overview of a method for a compression of elemental images in the photon counting three-dimensional (3D) integral imaging.

Lossless Compression of Elemental Images in Photon Counting Three-dimensional Integral Imaging

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Partially occluded object reconstruction using multiple Kinect sensors

Lossless Compression of Elemental Images in Photon Counting Three-dimensional Integral Imaging