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

In this paper, we propose a new method for 3D object reconstruction using an 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 an 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. IV International Conference on "Information Technology and Nanotechnology" (ITNT-2018) ICP algorithm [15]; a new registration algorithm based on the Matching Signed Distance Fields [16]. In order to fill small holes and to eliminate noise, the median and binomial filters were used [17, 18, 19, 20, 21, 22]. Moreover, the use of the color information in the point correspondence process avoids false positives matches and, therefore, leads to a more reliable registration. Note that by adjusting ICP and reconstruction parameters it is possible to improve the registration and appearance of details that were invisible with just one scan due to the sensor limited precision. Finally, it was shown that with help of low precision sensors as Kinect 3D smooth surface of objects can be reconstructed [23]. A new low-cost approach to reconstruct real-time of a 3D object with Kinect sensor uses a SLAM algorithm (Simultaneous Localization and Mapping) [24]. SLAM provides an approximated solution of 3D reconstruction because the accuracy of the system often depends on a heuristic algorithm for obtaining relevant reference points. In order to improve the accuracy and robustness of the ICP algorithm, a regularization by incorporating the spatial distances of SIFT feature pairs with dynamically adjusted weights to balance errors is proposed in [12]. A new outlier rejection method based on a dynamic thresholding and leverages the structure and sparse feature pairs from the texture of the RGB images were also suggested [12]. A new method to robustly estimates the camera motion in a dynamic environment based on RANSAC algorithm was proposed in the article [25]. We propose to utilize a graph-based SLAM algorithm [24] with loop closure detection using dense color and depth images obtained from the RGB-D camera. We show that our system is able to perform the SLAM for three-dimensional modeling in real-time. The paper is organized as follows. Section 2 discusses related works. Section 3 describes the proposed system for object 3D reconstruction using an RGB-D sensor. In Section 4 the experimental results are discussed. Finally, Section 5 presents our conclusions.