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This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering.

In this paper, virtual reality development software Virtools is used as the main tool for scene rendering in distributed virtual reality systems. The nearest distance-based control strategy is designed to achieve synchronous interaction between nodes by utilizing consistency control and prediction algorithms for nodes in the same cluster. Optimize the virtual scene by using texture mapping, detail level, and dynamic loading techniques. Propose a distributed parallel drawing method based on image plane segmentation to accelerate image drawing. The analysis and testing of the multiuser virtual reality roaming system in this paper is completed from four aspects: offline training model evaluation amount roaming test, lock performance test, and operation sequence error correction test. Among the analyzed data, the average lock time overhead is 123us, which has no obvious connection with the number of nodes, the number of locking times, and the execution of operations. According to each lock overhead of 123us, the number of concurrencies allowed per second is 8563, which fully meets the requirements of the system at this stage.

In this paper, we use Bayesian model to calculate color probability, train Bayesian classifier using a skin color training set of different gestures, and realize full skin color foreground object segmentation. The rendering of depth texture is established throughout to realize the unified scene rendering of art sketching teaching space and combined with a deep convolutional neural network to realize the interactive operation of virtual reality sketching teaching. Sampled students of T1 and T2 groups of art majors in Q College were analyzed for the acceptance of the virtual teaching scene of art sketching and explored the usability of virtual teaching of art sketching. Analyze and test the application effect of virtual teaching of art sketching from the two aspects of student’s learning motivation and the results of sketching work innovation assessment. The virtual simulation course of art sketching is designed to meet demand innovation by combining the total score of SUS and the application results. Based on the total SUS score of 62.325 for virtual teaching of art sketching, we added expert scoring data in the course design and obtained the difference between student scoring and expert scoring in terms of ease of operation, R42, and smoothness of operation and interaction, R23, which are only 0.01 and 0.06 respectively, which indicates that the virtual simulation course of art sketching should pay more attention to ease of operation and smoothness of operation. It shows that the virtual simulation course for art sketching should pay more attention to the ease of teaching and smoothness of operation and interaction.

The traditional rendering technology creates virtual scenes with insufficient fidelity, which are quite different from real scenes. To address this issue, a super-resolution virtual scene rendering technology based on generalized Huber-MRF image modeling has been studied. This study preprocesses the original image through three steps: graying, filtering, and enhancement. The generalized Huber-MRF is employed for super-resolution image restoration to enhance image clarity. Corner features are extracted from the super-resolution image, and the Delaunay triangular grid method is used to construct the image's 3D model. Texture and lighting conditions of the virtual scene are then set through texture mapping, shadow rendering, and other technologies to achieve realistic scene effects. The results indicate that, when applied, the research technology yields a relatively small chamfer distance in virtual scene modeling, suggesting that the design method preserves the details and shape information of the original image, reducing the difference between the virtual scene and the real scene and increasing the fidelity of the virtual scene. Furthermore, this method achieves maximum PSNR and SSIM values of 17.54 and 0.978, respectively, with an image preprocessing time of only 1.21 s and a CPU utilization rate of only 35.5%. This method demonstrates excellent performance across multiple aspects.

Large-scale scene rendering faces challenges in managing massive scene data and mitigating rendering latency caused by suboptimal loading sequences. Although current approaches utilize Level of Detail (LOD) for dynamic resource loading, two limitations remain. One is loading priority, which does not adequately consider the factors affecting visual effects such as LOD selection and visible area. The other is the insufficient trade-off between rendering quality and loading latency. To this end, we propose a loading prioritization metric called Vision Degree (VD), derived from LOD selection, loading time, and the trade-off between rendering quality and loading latency. During rendering, VDs are sorted in descending order to achieve an optimized loading and unloading sequence. At the same time, a compensation factor is proposed to further compensate for the visual loss caused by the reduced LOD level and to optimize the rendering effect. Finally, we optimize the initial viewpoint selection by minimizing the average model-to-viewpoint distance, thereby reducing the initial scene loading time. Experimental results demonstrate that our method reduces the rendering latency by 24–29% compared with the existing Area-of-Interest (AOI)-based loading strategy, while maintaining comparable visual quality.

The paper synoptically discusses the basic technological process of the ray tracing method, analyzes its parallel algorithms. Introduce the recent research focus of the field from study on real-time ray tracing and based ray tracing of graphics hardware. Point out the advantages and disadvantages of the ray tracing algorithms to solve scene rendering as well as future research priorities.

Based on the study of pure surface texture mapping technology, pure texture surface rendering method is proposed. The method is combined pure surface texture rendering and view mirror, real-time rendering has an index of refraction, reflection, and the flow of water ripple effect. Through the experimental verification of the validity of the algorithm. [PUBLICATION ABSTRACT]

Base on the analysis of open source graphics engine OGRE, the means to realize the flight visual emulator was researched and discussed. The thought and method on aircraft module making, flight scene establishing and Real-time flight state realizing were taken out. Through actual application of these ideas, the realization effect which was low-cost, run smoothly and less demanding to computer hardware in the flight visual emulator was obtained.

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner.

The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into images. Apart from fast rendering, the explicit representation of 3D Gaussian splatting also facilitates downstream tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid changes and growing number of works in this field, we present a literature review of recent 3D Gaussian splatting methods, which can be roughly classified by functionality into 3D reconstruction, 3D editing, and other downstream applications. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian splatting are also covered to aid understanding of this technique. This survey aims to help beginners to quickly get started in this field and to provide experienced researchers with a comprehensive overview, aiming to stimulate future development of the 3D Gaussian splatting representation.