Generative Adversarial Networks (GANs) for 3D volume generation and reconstruction, such as shape generation, visualization, automated design, real-time simulation, and research applications, are receiving increased amounts of attention in various fields. However, challenges such as limited training data, high computational costs, and mode collapse issues persist. We propose combining a Variational Autoencoder (VAE) and a GAN to uncover enhanced 3D structures and introduce a stable and scalable progressive growth approach for generating and reconstructing intricate voxel-based 3D shapes. The cascade-structured network involves a generator and discriminator, starting with small voxel sizes and incrementally adding layers, while subsequently supervising the discriminator with ground-truth labels in each newly added layer to model a broader voxel space. Our method enhances the convergence speed and improves the quality of the generated 3D models through stable growth, thereby facilitating an accurate representation of intricate voxel-level details. Through comparative experiments with existing methods, we demonstrate the effectiveness of our approach in evaluating voxel quality, variations, and diversity. The generated models exhibit improved accuracy in 3D evaluation metrics and visual quality, making them valuable across various fields, including virtual reality, the metaverse, and gaming.

Indoor localization is a basic element in location-based services (LBSs), including seamless indoor and outdoor navigation, location-based precision marketing, spatial recognition in robotics, augmented reality, and mixed reality. The popularity of LBSs in the augmented reality and mixed reality fields has increased the demand for a stable and efficient indoor positioning method. However, the problem of indoor visual localization has not been appropriately addressed, owing to the strict trade-off between accuracy and cost. Therefore, we use point cloud and RGB characteristic information for the accurate acquisition of three-dimensional indoor space. The proposed method is a novel visual positioning system (VPS) capable of determining the user’s position by matching the pose information of the object estimated by the improved point-graph neural network (GNN) with the pose information label of a voxel database object addressed in predefined voxel units. We evaluated the performance of the proposed system considering a stationary object in indoor space. The results verify that high positioning accuracy and direction estimation can be efficiently achieved. Thus, spatial information of indoor space estimated using the proposed novel VPS can aid in indoor navigation.

Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph. Our network provides the required details to analyze, generate and estimate bounding boxes by spatially structuring the input data into graphs. Our method proposes a keypoint attention mechanism that aggregates the relative features between each point to estimate the category and pose of the object to which the vertices of the graph belong, and also designs nine degrees of freedom of multi-object pose estimation. In addition, to avoid gimbal lock in 3D space, we use quaternion rotation, instead of Euler angle. Experimental results showed that memory usage and efficiency could be improved by aggregating point features from the point cloud and their neighbors in a graph structure. Overall, the system achieved comparable performance against state-of-the-art systems.

Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponentially. This rapid increase in complexity has led to problems with recording and transmission. In this study, we propose a method of efficiently managing and compressing animation information stored in the 3D point-clouds sequence. A compressed point-cloud is created by reconfiguring the points based on their voxels. Compared with the original point-cloud, noise caused by errors is removed, and a preprocessing procedure that achieves high performance in a redundant processing algorithm is proposed. The results of experiments and rendering demonstrate an average file-size reduction of 40% using the proposed algorithm. Moreover, 13% of the over-lap data are extracted and removed, and the file size is further reduced.

Recently, the development of 3D graphics technology has led to various technologies being combined with reality, where a new reality is defined or studied; they are typically named by combining the name of the technology with “reality”. Representative “reality” includes Augmented Reality, Virtual Reality, Mixed Reality, and eXtended Reality (XR). In particular, research on XR in the web environment is actively being conducted. The Web eXtended Reality Device Application Programming Interface (WebXR Device API), released in 2018, allows instant deployment of XR services to any XR platform requiring only an active web browser. However, the currently released tentative version has poor stability. Therefore, in this study, the performance evaluation of WebXR Device API is performed using three experiments. A camera trajectory experiment is analyzed using ground truth, we checked the standard deviation between the ground truth and WebXR for the X, Y, and Z axes. The difference image experiment is conducted for the front, left, and right directions, which resulted in a visible difference image for each image of ground truth and WebXR, small mean absolute error, and high match rate. In the experiment for measuring the 3D rendering speed, a frame rate similar to that of real-time is obtained.

Recently, the demand for location-based services using mobile devices in indoor spaces without a global positioning system (GPS) has increased. However, to the best of our knowledge, solutions that are fully applicable to indoor positioning and navigation and ensure real-time mobility on mobile devices, such as global navigation satellite system (GNSS) solutions, cannot achieve remarkable researches in indoor circumstances. Indoor single-shot image positioning using smartphone cameras does not require a dedicated infrastructure and offers the advantages of low price and large potential markets owing to the popularization of smartphones. However, existing methods or systems based on smartphone cameras and image algorithms encounter various limitations when implemented in indoor environments. To address this, we designed an indoor visual positioning system for mobile devices that can locate users in indoor scenes. The proposed method uses a smartphone camera to detect objects through a single image in a web environment and calculates the location of the smartphone to find users in an indoor space. The system is inexpensive because it integrates deep learning and computer vision algorithms and does not require additional infrastructure. We present a novel method of detecting 3D model objects from single-shot RGB data, estimating the 6D pose and position of the camera and correcting errors based on voxels. To this end, the popular convolutional neural network (CNN) is improved by real-time pose estimation to handle the entire 6D pose estimate the location and direction of the camera. The estimated position of the camera is addressed to a voxel to determine a stable user position. Our VPS system provides the user with indoor information in 3D AR model. The voxel address optimization approach with camera 6D position estimation using RGB images in a mobile web environment outperforms real-time performance and accuracy compared to current state-of-the-art methods using RGB depth or point cloud.