Scientific and technological advances during the last decade in the fields of image acquisition, data processing, telecommunications, and computer graphics have contributed to the emergence of new multimedia, especially 3D digital data. Modern 3D imaging technologies allow for the acquisition of 3D and 4D (3D video) data at higher speeds, resolutions, and accuracies. With the ability to capture increasingly complex 3D/4D information, advancements have also been made in the areas of 3D data processing (e.g., filtering, reconstruction, compression). As such, 3D/4D technologies are now being used in a large variety of applications, such as medicine, forensic science, cultural heritage, manufacturing, autonomous vehicles, security, and bioinformatics. Further, with mixed reality (AR, VR, XR), 3D/4D technologies may also change the ways we work, play, and communicate with each other every day.
In this paper, we propose a mesh-based feature detection scheme that focuses on surface features. A class of features of key interest is intentional structures that act as fiducials and that, for instance, can assist in shape retrieval and distortion measurement. We introduce a tunable two-scale depth measurement scheme to quantify the displacement of a vertex from the local surface, which can be a strong indicator of features. We print and scan 3D models with fiducial features appearing across the surface to demonstrate the high fidelity and accuracy of the proposed feature detection scheme. The method outperforms existing 3D feature detection schemes on CAD models and 3D scans alike. We also discuss applications of data embedding enabled by the achievable detection performance.
In this work, we present a method to estimate the pose of teeth in pathological dental models. For each tooth of a pathological model, we aim at computing its orientation and position with respect to a healthy dentition. The proposed method is based on the registration of a reference model on a patient-specific 3D segmented mesh. Dental features, such as the arch forms and their types, are derived. These features, combined with registration information, allow our system to propose a plausible target dental arrangement and thus estimate the pose of each tooth. The key contributions of this work are (a) the use of a registered reference model to derive dental features and quantify orthodontic disorders and (b) the automatic estimation of a target dental arrangement.
The geometry comparison is promising for the deformation assessment of cultural heritage (CH) surfaces over the decade. In this work, the potential reliability of the developed changed-based segmentation method was explored in quantifying the deformation on an object. The proposed method was tested using two parts of a single model, considering one ideal in shape and the other part is deformed over time. This study explains how deformation using changed-based segmentation can be identified successfully with millimeter level accuracy in the measurement and inform future conservation treatment. The method is insensitive to the noise of surfaces and the cross-time alignment of two models with a known reference of no change. The technique allows identifying the deterioration based on deformation detection with clear colormap visualization and its significance as a preventive measure without using a physical marker as a reference. The threshold values were fed to the method based on the known respect of no change and quantified as minor and major based on the object's size. The presented result in this paper suggests that the method can be effectively used to enhance 3D documentation of monitoring both indoor and outdoor environments and perform preventive interventions irrespective of the object's size.
The ever-growing variety of capture methods and applications for 3D range geometry continually increases the need for efficient methods of data storage and transmission. Compression techniques address this need by offering reduced file sizes while maintaining the precision needed for a particular application. Several such compression methods use phase-shifting principles to encode the 3D data into a 2D RGB image. In some applications, such as telepresence, high precision may only be required in a particular region within a scan. This paper proposes a feature-driven compression method that provides a way to encode regions of interest at higher precision while encoding the remaining data at lower precision to reduce file sizes. This method supports both lossless and lossy compression, enabling even greater file size savings and a wider range of applications. In the case of a depth scan of a bust, an extracted bounding box of the face was used to create an encoding distribution such that the facial region was encoded at higher precisions. When using JPEG 80, the global RMS reconstruction accuracy of this novel encoding was 99.72%; however, in the region of interest, the accuracy was 99.88%. This feature-driven encoding achieved a 26% reduction in compressed file size compared to a fixed, high precision encoding.
In this paper, a subjective quality based comparison between four point clouds codecs is presented. For that, a set of six point clouds was chosen. They were coded with four different point cloud encoding solutions, notably the MPEG V-PCC and G-PCC, a deep learning coding solution RS-DLPCC and also Draco, with different bit rates. A subjective test where the distorted and reference point clouds were rotated in a video sequence side by side followed by the quality evaluation, was conducted. Then the performance of a set of four point cloud objective quality metrics of he quality, was analysed using the subjective quality evaluation results. These metrics are usually reported as providing a good representation and are often used to evaluate compression solutions. In fact, the studied metrics tend to provide a good representation for V-PCC and G-PCC, an acceptable representation for RS-DLPCC, and a bad representation for Draco. It was also concluded that V-PCC is the best codec of the studied ones. The deep learning based solution still performs worst than the two MPEG codecs.
In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications. As the behavioral biometrics, many researches were performed on voiceprints. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only either color images or depth images are used for hand gesture authentication in the conventional methods. In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is a deep learning-based network, is used to extract individual habits. An F-measure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. An F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.
Digital archaeology is a rapidly evolving field, adapting new technologies to interpret diverse data sources. This paper details the superimposition of 2D maps and 3D data in an interactive 3D space, and their selective subtraction by a 3D brush system. The subject of study is the archaeological landscape of the medieval city of Angkor in Cambodia, an area of approximately 3500 square kilometers. By cutting through the superimposed layers of LIDAR point clouds, 2D mapping of the archaeological features, and the 3D reconstructions of the living city of Angkor, the brush system reveals both correspondences and discontinuities through interactive examination.
In a dihedral corner reflector array (DCRA) used as an aerial display, the observer can directly touch aerial images. However, in this optical element, the visibility of the aerial image is reduced by stray light, called a ghost. Although ghost formation can be suppressed using a louver, the viewing angle and brightness of the aerial image also decrease. This paper proposes the design of a ghost-free aerial display using a prism and DCRA. Based on experimental results, the ghost brightness with the proposed method was only about 14% of the DCRA at front. In addition, the aerial image produced by the proposed device was the brightest for 0–20°.