360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360∘^\circ Radiance Fields

Virtual tour among sparse 360$^\circ$ images is widely used while hindering smooth and immersive roaming experiences. The emergence of Neural Radiance Field (NeRF) has showcased significant progress in synthesizing novel views, unlocking the potential for immersive scene exploration. Nevertheless, previous NeRF works primarily focused on object-centric scenarios, resulting in noticeable performance degradation when applied to outward-facing and large-scale scenes due to limitations in scene parameterization. To achieve seamless and real-time indoor roaming, we propose a novel approach using geometry-aware radiance fields with adaptively assigned local radiance fields. Initially, we employ multiple 360$^\circ$ images of an indoor scene to progressively reconstruct explicit geometry in the form of a probabilistic occupancy map, derived from a global omnidirectional radiance field. Subsequently, we assign local radiance fields through an adaptive divide-and-conquer strategy based on the recovered geometry. By incorporating geometry-aware sampling and decomposition of the global radiance field, our system effectively utilizes positional encoding and compact neural networks to enhance rendering quality and speed. Additionally, the extracted floorplan of the scene aids in providing visual guidance, contributing to a realistic roaming experience. To demonstrate the effectiveness of our system, we curated a diverse dataset of 360$^\circ$ images encompassing various real-life scenes, on which we conducted extensive experiments. Quantitative and qualitative comparisons against baseline approaches illustrated the superior performance of our system in large-scale indoor scene roaming

Time-of-Day Neural Style Transfer for Architectural Photographs

Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background. Inspired by recent successes in image-to-image translation methods, we aim to perform style transfer for architectural photographs. However, the special composition in architectural photography poses great challenges for style transfer in this type of photographs. Existing neural style transfer methods treat the architectural images as a single entity, which would generate mismatched chrominance and destroy geometric features of the original architecture, yielding unrealistic lighting, wrong color rendition, and visual artifacts such as ghosting, appearance distortion, or color mismatching. In this paper, we specialize a neural style transfer method for architectural photography. Our method addresses the composition of the foreground and background in an architectural photograph in a two-branch neural network that separately considers the style transfer of the foreground and the background, respectively. Our method comprises a segmentation module, a learning-based image-to-image translation module, and an image blending optimization module. We trained our image-to-image translation neural network with a new dataset of unconstrained outdoor architectural photographs captured at different magic times of a day, utilizing additional semantic information for better chrominance matching and geometry preservation. Our experiments show that our method can produce photorealistic lighting and color rendition on both the foreground and background, and outperforms general image-to-image translation and arbitrary style transfer baselines quantitatively and qualitatively. Our code and data are available at https://github.com/hkust-vgd/architectural_style_transfer.Comment: Updated version with corrected equations. Paper published at the International Conference on Computational Photography (ICCP) 2022. 12 pages of content with 6 pages of supplementary material

Advances in 3D Neural Stylization: A Survey

Modern artificial intelligence provides a novel way of producing digital art in styles. The expressive power of neural networks enables the realm of visual style transfer methods, which can be used to edit images, videos, and 3D data to make them more artistic and diverse. This paper reports on recent advances in neural stylization for 3D data. We provide a taxonomy for neural stylization by considering several important design choices, including scene representation, guidance data, optimization strategies, and output styles. Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then provides in-depth discussions on recent neural stylization methods for 3D data, where we also provide a mini-benchmark on artistic stylization methods. Based on the insights gained from the survey, we then discuss open challenges, future research, and potential applications and impacts of neural stylization.Comment: 26 page

Neural Scene Decoration from a Single Photograph

Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty indoor space and a list of decorations with layout determined by user, we aim to synthesize a new image of the same space with desired furnishing and decorations. Neural scene decoration can be applied to create conceptual interior designs in a simple yet effective manner. Our attempt to this research problem is a novel scene generation architecture that transforms an empty scene and an object layout into a realistic furnished scene photograph. We demonstrate the performance of our proposed method by comparing it with conditional image synthesis baselines built upon prevailing image translation approaches both qualitatively and quantitatively. We conduct extensive experiments to further validate the plausibility and aesthetics of our generated scenes. Our implementation is available at \url{https://github.com/hkust-vgd/neural_scene_decoration}.Comment: ECCV 2022 paper. 14 pages of main content, 4 pages of references, and 11 pages of appendi

Recent Advances in the Study of the Immune Escape Mechanism of SFTSV and Its Therapeutic Agents

Sever fever with thrombocytopenia syndrome (SFTS) is a new infectious disease that has emerged in recent years and is widely distributed, highly contagious, and lethal, with a mortality rate of up to 30%, especially in people with immune system deficiencies and elderly patients. SFTS is an insidious, negative-stranded RNA virus that has a major public health impact worldwide. The development of a vaccine and the hunt for potent therapeutic drugs are crucial to the prevention and treatment of Bunyavirus infection because there is no particular treatment for SFTS. In this respect, investigating the mechanics of SFTS–host cell interactions is crucial for creating antiviral medications. In the present paper, we summarized the mechanism of interaction between SFTS and pattern recognition receptors, endogenous antiviral factors, inflammatory factors, and immune cells. Furthermore, we summarized the current therapeutic drugs used for SFTS treatment, aiming to provide a theoretical basis for the development of targets and drugs against SFTS

The Effect Analysis of Water Diversion on Water Quality Improvement: A Case Study in Urban Lake, China

With the rapid progress of society and economy on a regional level, river pollution trends have risen, causing an overwhelmingly poor water quality in urban lakes. In this study, a two-dimensional coupled hydrodynamic and water quality model was employed to assess the enhancement of water quality subsequent to the implementation of water diversion measures in Lake Hou, a representative urban lake located in Wuhan. The model was established based on detailed data collection via survey analysis, model simulation, and joint analysis. The total amount of pollutants in Lake Hou before and after pollution interception and control was compared and analyzed. The observed lake water level, discharge, and water quality parameters, including total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD), were utilized to evaluate the performance of the model. The results showed that the water quality of Lake Hou improved as the recharge flow increased. When the recharge flow was 10 m3/s, TP, TN, and COD improvements were 28.94%, 24.14%, and 14.30%, respectively. When the recharge flow was 15 m3/s, TP, TN, and COD improvements were 33.14%, 27.77%, and 15.57%, respectively. When the recharge flow was 20 m3/s, TP, TN, and COD improvements were 35.74%, 30.10%, and 16.29%, respectively. However, a downward trend can be observed with increasing rates of TP, TN, and COD improvements (%), from 10 m3/s to 15 m3/s, at 4.2%, 3.56%, and 1.27%, respectively. The increasing rates of TP, TN, and COD improvements (%) from 15 m3/s to 20 m3/s were 2.6%, 2.4%, and 0.27%, respectively. This study offers a valuable technical solution for the management of urban lakes

Static and Dynamic Characteristic Simulation of Feed System Driven by Linear Motor in High Speed Computer Numerical Control Lathe

In order to design the feed system of high speed Computer Numerical Control (CNC) lathe, the static and dynamic characteristics of feed system driven by linear motor in high speed CNC lathe were analyzed. The slide board was taking as the main moving part of the feed system, and the guide rail was the main support component of the linear motor feed system. The mechanical structure static stiffness of feed system is researched through the slide board statics analysis. The simulation results show that the maximum deformation of the slide board occurs in the middle of the slide board where the linear motor is placed. The linear motor feed system control model was established based on analysis of high-speed linear feed system control principle, and the linear motor feed system transfer function was established, and servo dynamic stiffness factors were analyzed. The control parameters of the servo system and actuating mechanism parameters of feed system on the effect of the linear motor servo dynamic stiffness were analyzed using MATLAB software. The simulation results show that the position loop proportional gain, speed loop proportional gain and speed loop integral response time are the biggest influence factors on servo dynamic stiffness. The displacement response is reduced under the cutting interference force step inputting, the servo dynamic stiffness is increased, the number of system oscillation is also reduced, and the system tends to be stable. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.281

Internet of Things, Linked Data, and Citizen Participation as Enablers of Smarter Cities

Open Access articleWeb of Data and Internet of Things are enabling technologies that pave the way for next generation urban services. These services will play a crucial role in future interactions between the city and the citizens, giving them the impression of facing Smarter Cities, that is, cities that not only do manage their resources more efficiently but also are aware of the citizen needs. The aim of this special issue has been to bring together research results on the areas of Linked Data, Internet of Things, and smartphone-mediated interaction to assemble service ecosystems that may give place to Smarter Cities, that is, those that are actually aware of the real needs and demands of their citizens. This special issue includes 6 articles covering the topics of crowdsourcing, that is, cooperation of individuals and IT systems to provide solutions where machine automation cannot reach data correlation of big volumes of data to enhance mobility in cities, analysis of access logs to both social networks and IT systems in order to predict the next location or the next data chunk to be requested by a user, and, finally, novel AI techniques to explore interdependencies among different factors to help in the decision making process within a city. The article entitled “CooperSense: A Cooperative and Selective Picture Forwarding Framework Based on Tree Fusion” by H. Chen et al. explores the topic of mobile crowd photographing for local sensing, allowing encountering participants to only exchange those pictures relevant to each other by applying a tree based selection mechanism. The article “A Context-Driven Worker Selection Framework for Crowd-Sensing” by J. Wang et al. proposes a novel worker selection framework, called WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creators define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. The article entitled “Urban Impedance Computing Based on Check-In Records” by Z. Yu et al. analyses the concept of urban impedance, that is, the travelling cost between the origin and destination locations as an important indicator of urban accessibility. For that, it combines check-in records obtained from mobile social networks and road networks data to calculate and adjust the various parameters of the model, including path length, number and angle of turns, number and direction of junctions, and population density. The article “Twitter Can Predict Your Next Place of Visit” by A. Chauhan et al. proposes a predictor for users' next place of visit using their past tweets. For that, it computes the probabilities of visiting different types of places using a bank of binary classifiers and Markov models. The article “Prefetching Scheme for Massive Spatiotemporal Data in a Smart City” by L. Xiong et al. explores access patterns to develop a prefetching scheme, which can effectively improve system I/O performance and reduce user access latency. A prefetching scheme based on spatial-temporal attribute prediction, called STAP, is developed which maps the history of user access requests to the spatiotemporal attribute domain by analysing the characteristics of spatiotemporal data in a smart city. Notably, the STAP scheme mines the user access patterns and constructs a predictive function to predict the user's next access request. Finally, the article “Factor Knowledge Mining Using the Techniques of AI Neural Networks and Self-Organizing Map” by P.-K. Wu and T.-C. Hsiao offers a hybrid technique combining artificial neural networks (ANN) and self-organizing maps (SOM) as a way to explore factor knowledge, namely NNSOM. This technique is applied to analyse the most important factor to organize a night market in Taiwan. Overall, this special issue explores how to bring together machine and human intelligence in order to understand better data flows in the context of a city and support the decision making process within the cities of the future