End-to-End Paired Ambisonic-Binaural Audio Rendering

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

Exploring Frontier Technologies in Video-Based Person Re-Identification:A Survey on Deep Learning Approach

Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the field.In order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function design.Firstly,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these methods.Furthermore,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation.Lastly,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID.This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.

A Fast and Memory-Efficient Direct Rendering Method for Polynomial-Based Implicit Surfaces

Three-dimensional surfaces are typically modeled as implicit surfaces.However,direct rendering of implicit surfaces is not simple,especially when such surfaces contain finely detailed shapes.One approach is ray-casting,where the field of the implicit surface is assumed to be piecewise polynomials defined on the grid of a rectangular domain.A critical issue for direct rendering based on ray-casting is the computational cost of finding intersections between surfaces and rays.In particular,ray-casting requires many function evaluations along each ray,severely slowing the rendering speed.In this paper,a method is proposed to achieve direct rendering of polynomial-based implicit surfaces in real-time by strategically narrowing the search range and designing the shader to exploit the structure of piecewise polynomials.In experiments,the proposed method achieved a high framerate performance for different test cases,with a speed-up factor ranging from 1.1 to 218.2.In addition,the proposed method demonstrated better efficiency with high cell resolution.In terms of memory consumption,the proposed method saved between 90.94%and 99.64%in different test cases.Generally,the proposed method became more memoryefficient as the cell resolution increased.

Research and Application of Caideng Model Rendering Technology for Virtual Reality

With the development of virtual reality (VR) technology, more and more industries are beginning to integrate with VR technology. In response to the problem of not being able to directly render the lighting effect of Caideng in digital Caideng scenes, this article analyzes the lighting model. It combines it with the lighting effect of Caideng scenes to design an optimized lighting model algorithm that fuses the bidirectional transmission distribution function (BTDF) model. This algorithm can efficiently render the lighting effect of Caideng models in a virtual environment. And using image optimization processing methods, the immersive experience effect on the VR is enhanced. Finally, a Caideng roaming interactive system was designed based on this method. The results show that the frame rate of the system is stable during operation, maintained above 60 fps, and has a good immersive experience.

A survey of real-time rendering on Web3D application

Background In recent years, with the rapid development of mobile Internet and Web3D technologies, a large number of web-based online 3D visualization applications have emerged. Web3D applications, including Web3D online tourism, Web3D online architecture, Web3D online education environment, Web3D online medical care, and Web3D online shopping are examples of these applications that leverage 3D rendering on the web. These applications have pushed the boundaries of traditional web applications that use text, sound, image, video, and 2D animation as their main communication media, and resorted to 3D virtual scenes as the main interaction object, enabling a user experience that delivers a strong sense of immersion. This paper approached the emerging Web3D applications that generate stronger impacts on people's lives through “real-time rendering technology”, which is the core technology of Web3D. This paper discusses all the major 3D graphics APIs of Web3D and the well-known Web3D engines at home and abroad and classify the real-time rendering frameworks of Web3D applications into different categories. Results Finally, this study analyzed the specific demand posed by different fields to Web3D applications by referring to the representative Web3D applications in each particular field. Conclusions Our survey results show that Web3D applications based on real-time rendering have in-depth sectors of society and even family, which is a trend that has influence on every line of industry.

An Improved HVQ Algorithm for Compression and Rendering of Space Environment Volume Data with Multi-correlated Variables

Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.

Perceptual Optimization for Point-Based Point Cloud Rendering

Point-based rendering is a common method widely used in point cloud rendering.It realizes rendering by turning the points into the base geometry.The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry,usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point.This method effectively reduces the appearance of empty spaces between points in rendering.However,it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large,which impacts the perceptual quality.To solve the above problem,we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering.The algorithm determines whether the detected points are outliers using a combination of local and global geometric features.For the detected outliers,the minimum radius is used for rendering.We examine the performance of the proposed method in terms of both objective quality and perceptual quality.The experimental results show that the peak signal-to-noise ratio(PSNR)of the point cloud sequences is improved under all geometric quantization,and the PSNR improvement ratio is more evident in dense point clouds.Specifically,the PSNR of the point cloud sequences is improved by 3.6%on average compared with the original algorithm.The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.

Rendering algorithms for aberrated human vision simulation

Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is that it is imperfect because of the highly influential wavefront aberrations that vary from person to person. This study provides an overview of the existing computational image generation techniques that properly simulate human vision in the presence of wavefront aberrations. These algorithms typically apply ray tracing with a detailed description of the simulated eye or utilize the point-spread func-tion of the eye to perform convolution on the input image. Based on the description of the vision simulation tech-niques, several of their characteristic features have been evaluated and some potential application areas and research directions have been outlined.

Video-Based Interventions for Adolescents and Young Adults with Autism Spectrum Disorder:A Systematic

Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.

Web-based mixed reality video fusion with remote rendering

Background Mixed reality(MR)video fusion systems merge video imagery with 3D scenes to make the scene more realistic and help users understand the video content and temporal–spatial correlation between them,reducing the user′s cognitive load.MR video fusion are used in various applications;however,video fusion systems require powerful client machines because video streaming delivery,stitching,and rendering are computationally intensive.Moreover,huge bandwidth usage is another critical factor that affects the scalability of video-fusion systems.Methods Our framework proposes a fusion method for dynamically projecting video images into 3D models as textures.Results Several experiments on different metrics demonstrate the effectiveness of the proposed framework.Conclusions The framework proposed in this study can overcome client limitations by utilizing remote rendering.Furthermore,the framework we built is based on browsers.Therefore,the user can test the MR video fusion system with a laptop or tablet without installing any additional plug-ins or application programs.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Fabrication of High Color Rendering Index White Light Emitting Diodes from Gold Nanoclusters

We demonstrated gold nanoclusters as color tunable emissive light converters for the application of white light emitting diodes (WLEDs). A blue LED providing 460 nm to excite gold nanoclusters mixed with UV curable material generates broad bandwidth emission at the visible range. Increasing the amount of gold nanoclusters, the correlated color temperature of WLEDs tuned from cold white to warm white, and also results in the variation of color rendering index (CRI). The highest CRI in the experiment is 92.

经阴道超声联合3D-TVS Render成像诊断子宫内膜病变的临床价值分析

目的分析经阴道超声联合三维超声(3D-TVS)Render成像诊断子宫内膜病变的临床价值。方法回顾性分析2019年11月至2022年11月我院收治的123例子宫内膜病变患者的临床资料,术前或诊刮前均行经阴道超声、3D-TVS Render成像检查,以病理检查结果作为“金标准”,分析经阴道超声联合3D-TVS Render成像对子宫内膜病变的诊断价值。结果经病理检查结果显示,123例患者中子宫内膜癌24例,子宫内膜良性病变99例;子宫内膜癌患者经阴道超声检查的子宫内膜厚度(ET)高于良性病变患者,经3D-TVS Render成像检查的子宫内膜血管指数(VI)、血流指数(FI)、血管血流指数(VFI)高于良性病变患者(P<0.05);绘制ROC曲线结果显示,ET、VI、FI、VFI联合诊断子宫内膜病变的AUC为0.968,高于各指标单独诊断。结论经阴道超声联合3D-TVS Render成像对子宫内膜病变的诊断价值较高,值得临床推广应用。

On-line Free-viewpoint Video:From Single to Multiple View Rendering

In recent years, many image-based rendering techniques have advanced from static to dynamic scenes and thus become video-based rendering （VBR） methods. But actually, only a few of them can render new views on-line. We present a new VBR system that creates new views of a live dynamic scene. This system provides high quality images and does not require any background subtraction. Our method follows a plane-sweep approach and reaches real-time rendering using consumer graphic hardware, graphics processing unit （GPU）. Only one computer is used for both acquisition and rendering. The video stream acquisition is performed by at least 3 webcams. We propose an additional video stream management that extends the number of webcams to 10 or more. These considerations make our system low-cost and hence accessible for everyone. We also present an adaptation of our plane-sweep method to create simultaneously multiple views of the scene in real-time. Our system is especially designed for stereovision using autostereoscopic displays. The new views are computed from 4 webcams connected to a computer and are compressed in order to be transfered to a mobile phone. Using GPU programming, our method provides up to 16 images of the scene in real-time. The use of both GPU and CPU makes this method work on only one consumer grade computer.

SPEEDING-UP RE-SAMPLED ALGORITHM IN RAY CASTING VOLUME RENDERING OF MEDICAL IMAGES

Ray casting algorithm can obtain a better quality image in volume rendering, however, it exists some problems, such as powerful computing capacity and slow rendering speed. How to improve the re-sampled speed is a key to speed up the ray casting algorithm. An algorithm is introduced to reduce matrix computation by matrix transformation characteristics of re-sampling points in a two coordinate system. The projection of 3-D datasets on image plane is adopted to reduce the number of rays. Utilizing boundary box technique avoids the sampling in empty voxel. By extending the Bresenham algorithm to three dimensions, each re-sampling point is calculated. Experimental results show that a two to three-fold improvement in rendering speed using the optimized algorithm, and the similar image quality to traditional algorithm can be achieved. The optimized algorithm can produce the required quality images, thus reducing the total operations and speeding up the volume rendering.

Very-High Color Rendering Index Hybrid White Organic Light-Emitting Diodes with Double Emitting Nanolayers

A very-high color rendering index white organic light-emitting diode(WOLED) based on a simple structure was successfully fabricated. The optimized device exhibits a maximum total efficiency of 13.1 and 5.4 lm/W at 1,000 cd/m2. A peak color rendering index of 90 and a relatively stable color during a wide range of luminance were obtained. In addition, it was demonstrated that the 4,40,400-tri(9-carbazoyl) triphenylamine host influenced strongly the performance of this WOLED.These results may be beneficial to the design of both material and device architecture for high-performance WOLED.

A Mixed-Depth Visual Rendering Method for Bleeding Simulation

The visual fidelity of bleeding simulation in a surgical simulator is critical since it will affect not only the degree of visual realism,but also the user’s medical judgment and treatment in real-life settings.The conventional marching cubes surface rendering algorithm provides excellent visual effect in rendering gushing blood,however,it is insufficient for blood flow,which is very common in surgical procedures,since in this case the rendered surface and depth textures of blood are rough.In this paper,we propose a new method called the mixed depth rendering for rendering blood flow in surgical simulation.A smooth height field is created to minimize the height difference between neighboring particles on the bleeding surface.The color and transparency of each bleeding area are determined by the number of bleeding particles,which is consistent with the real visual effect.In addition,there is no much extra computational cost.The rendering of blood flow in a variety of surgical scenarios shows that visual feedback is much improved.The proposed mixed depth rendering method is also used in a neurosurgery simulator that we developed.

Cluster parallel rendering based on encoded mesh

Use of compressed mesh in parallel rendering architecture is still an unexplored area, the main challenge of which is to partition and sort the encoded mesh in compression-domain. This paper presents a mesh compression scheme PRMC (Parallel Rendering based Mesh Compression) supplying encoded meshes that can be partitioned and sorted in parallel rendering system even in encoded-domain. First, we segment the mesh into submeshes and clip the submeshes’ boundary into Runs, and then piecewise compress the submeshes and Runs respectively. With the help of several auxiliary index tables, compressed submeshes and Runs can serve as rendering primitives in parallel rendering system. Based on PRMC, we design and implement a parallel rendering architecture. Compared with uncompressed representation, experimental results showed that PRMC meshes applied in cluster parallel rendering system can dramatically reduce the communication requirement.

使用Light Field Rendering实现森林的实时渲染及光照算法

森林的实时渲染及光照是视景系统中的一个难题.基于图像的渲染方法(IBR)由于渲染速度与模型复杂度无关,被广泛应用于场景重建.基于光流场(Light Field Rendering)的IBR技术,提出一种迭代投射算法来进行外形重建,实现了具有实时光影特征的森林效果.实验表明该算法结合了传统迭代、投射算法各自的优点,在质量和效率方面取得了平衡.

MonkeyTrail:A scalable video-based method for tracking macaque movement trajectory in daily living cages

Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.