A novel switching bilateral filtering algorithm for depth map

In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.The Russian Science Foundation (project #17-76-20045) financially supported the work

Accuracy analysis of 3D object reconstruction using RGB-D sensor

In this paper, we propose a new method for 3D object reconstruction using RGB-D sensor. The RGB-D sensor provides RGB images as well as depth images. Since the depth and RGB color images are captured with one sensor of a RGB-D camera placed in different locations, the depth image should be related to the color image. After matching of the images (registration), point-to-point corresponding between two images is found, and they can be combined and represented in the 3D space. In order to obtain a dense 3D map of the 3D object, we design an algorithm for merging information from all used cameras. First, features extracted from color and depth images are used to localize them in a 3D scene. Next, Iterative Closest Point (ICP) algorithm is used to align all frames. As a result, a new frame is added to the dense 3D model. However, the spatial distribution and resolution of depth data affect to the performance of 3D scene reconstruction system based on ICP. The presented computer simulation results show an improvement in accuracy of 3D object reconstruction using real data.This work was supported by the Russian Science Foundation, grant no. 17-76-20045

Real-time tracking of multiple objects with locally adaptive correlation filters

A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects withinvariancetopose,occlusion,clutter,andilluminationvariations. Thealgorithmemploysapredictionschemeandcomposite correlationfilters. Thefiltersaresynthesizedwiththehelpofaniterativealgorithm,whichoptimizesdiscriminationcapabilityfor each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.This work was supported by the Russian Science Foundation, grant no. 15-19-10010

Accurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICP

Основная статьяIn this paper, we propose a new method for 3D map reconstruction using the Kinect sensor based on multiple ICP. The Kinect sensor provides RGB images as well as depth images. Since the depth and RGB color images are captured by one Kinect sensor with multiple views, each depth image should be related to the color image. After matching of the images (registration), point-topoint corresponding between two depth images is found, and they can be combined and represented in the 3D space. In order to obtain a dense 3D map of the 3D indoor environment, we design an algorithm to combine information from multiple views of the Kinect sensor. First, features extracted from color and depth images are used to localize them in a 3D scene. Next, Iterative Closest Point (ICP) algorithm is used to align all frames. As a result, a new frame is added to the dense 3D model. However, the spatial distribution and resolution of depth data affect to the performance of 3D scene reconstruction system based on ICP. In this paper we automatically divide the depth data into sub-clouds with similar resolution, to align them separately, and unify in the entire points cloud. This method is called the multiple ICP. The presented computer simulation results show an improvement in accuracy of 3D map reconstruction using real data

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

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. Experiments and computer simulation are carried out using Matlab and Kinect V2.The Russian Science Foundation (project #17-76-20045) financially supported the work

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

Основная статьяIn 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 its the depth data. Next, the precision of coordinate mapping between sensors data for registration of depth and color images is evaluated. We test a system with four Kinect V2 sensors and present reconstruction accuracy results. Experiments and computer simulation are carried out using Matlab and Kinect V2