Point-SLAM: Dense Neural Point Cloud-based SLAM

We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner. We demonstrate that both tracking and mapping can be performed with the same point-based neural scene representation by minimizing an RGBD-based re-rendering loss. In contrast to recent dense neural SLAM methods which anchor the scene features in a sparse grid, our point-based approach allows dynamically adapting the anchor point density to the information density of the input. This strategy reduces runtime and memory usage in regions with fewer details and dedicates higher point density to resolve fine details. Our approach performs either better or competitive to existing dense neural RGBD SLAM methods in tracking, mapping and rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is available at https://github.com/tfy14esa/Point-SLAM.Comment: 17 Pages, 10 Figure

Historic Costume Simulation and its Application

This study highlights the potential of new technology as a means to provide new possibility for costumes in fragile condition to be utilised. The aim of this study is to create accurate digital duplicates of costumes from historical sources, and to explore the possibility of developing them as an exhibitory and educational method applying 3D apparel CAD and new media. To achieve this, three attributes for qualities of effective digital costumes were suggested: faithful reproduction, virtual fabrication, and interactive and stereographic appreciation. Based on these qualities, digital costumes and a PC application were produced and evaluated

Internet-based Medical Data Rendering and Image Enhancement Using Webgl and Apache Server

Internet-based medical data visualization has wide applications in distributed medical collaborations and treatment. It can be achieved through volume rendering technique, which is a key method for medical image exploration and has been applied to the clinical medical fields such as disease diagnosis and image-guided interaction.In this project, we implement some medical data processing and optical mapping methods for web-based medical data visualization and image enhancement. The Web Graphics Library (WebGL) is used with JavaScript for rendering 3D graphics in a web browser. WebGL supports GPU based volume rendering which is an efficient tool for visual analysis of medical data, which involves vertex shaders and fragment shaders. The vertex shader provides space coordinates, and the fragment shader provides color.Network-based volume rendering is used to visualize data in a 3D form. An image processing method is implemented to transfer the 3D dataset into multiple slices of 2D image data and WebGL is employed to render 3D medical data in web browsers. Volume rendering is accomplished using the volume ray casting algorithm implemented with WebGL2. We collect new medical data and process them to fit the web-based rendering environment. The submitted work will explain the process of preparing and loading medical data suitable to be rendered. All the visualized data can be enhanced with the developed methods to emphasize the image feature of interest. We also add new control points for optical mapping and rendering medical data in a web browser in real-time. The software platform is running on Apache Web Server for network-based data visualization. The developed image enhancements and property control methods can improve medical data visualization on web browsers, which will be helpful for internet-based medical data analysis and exploration, as well as medical diagnosis and treatment.https://ir.library.illinoisstate.edu/ursit/1000/thumbnail.jp

Service-oriented visualization applied to medical data analysis

With the era of Grid computing, data driven experiments and simulations have become very advanced and complicated. To allow specialists from various domains to deal with large datasets, aside from developing efficient extraction techniques, it is necessary to have available computational facilities to visualize and interact with the results of an extraction process. Having this in mind, we developed an Interactive Visualization Framework, which supports a service-oriented architecture. This framework allows, on one hand visualization experts to construct visualizations to view and interact with large datasets, and on the other hand end-users (e.g., medical specialists) to explore these visualizations irrespective of their geographical location and available computing resources. The image-based analysis of vascular disorders served as a case study for this project. The paper presents main research findings and reports on the current implementation status