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Accelerating Stereo Rendering via Image Reprojection and Spatio-Temporal Supersampling

IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2643-2652页

作者： Yang, Sipeng Zhuge, Junhao Ji, Jiayu Zhu, Qingchuan Jin, Xiaogang Zhejiang University State Key Lab of CAD&CG China

Achieving immersive virtual reality (VR) experiences typically requires extensive computational resources to ensure high-definition visuals, high frame rates, and low latency in stereoscopic rendering. This challenge is particularly pronounced for lower-tier and standalone VR devices with limited processing power. To accelerate rendering, existing supersampling and image reprojection techniques have shown significant potential, yet to date, no previous work has explored their combination to minimize stereo rendering overhead. In this paper, we introduce a lightweight supersampling framework that integrates image projection with spatio-temporal supersampling to accelerate stereo rendering. Our approach effectively leverages the temporal and spatial redundancies inherent in stereo videos, enabling rapid image generation for unshaded viewpoints and providing resolution-enhanced and anti-aliased images for binocular viewpoints. We first blend a rendered low-resolution (LR) frame with accumulated temporal samples to construct an high-resolution (HR) frame. This HR frame is then reprojected to the other viewpoint to directly synthesize a new image. To address disocclusions in reprojected images, we utilize accumulated history data and low-pass filtering for filling, ensuring high-quality results with minimal delay. Extensive evaluations on both the PC and the standalone device confirm that our framework requires short runtime to generate high-fidelity images, making it an effective solution for stereo rendering across various VR platforms. © 1995-2012 IEEE.

关键词： Stereo image processing

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Augmented Sphere Tracing for Real-time Editing Mega-scale Periodic Shell-lattice Structures

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COMPUTER-AIDED DESIGN 2025年 184卷

作者： Guo, Jiajie Li, Ming Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China

We propose an augmented sphere tracing (AST) pipeline that seamlessly integrates editing, rendering, and slicing of mega-scale periodic shell-lattice structures. Traditional STL-based pipelines face challenges such as time-consuming format conversions, high storage requirements, and complex blending issues between discrete lattice and shell components, often resulting in a loss of geometric accuracy. Alternatively, implicit-based pipelines excel at smooth modeling and robust Boolean operations but require inefficient and error-prone conversions of STL shells into implicit forms, complicating the rendering process. To address these issues, AST combines hybrid implicit lattice and mesh shell representations, eliminating the need for explicit 3D model construction and unnecessary geometric format conversions. It overcomes the major challenges of hybrid forms and mega-scale rendering by using an augmented tracing distance query that avoids costly signed distance field (SDF) calculations while preserving geometric details. Additionally, it employs a local tracing distance query within a single cell, leveraging lattice periodicity for efficiency. The pipeline also supports various types of shell-lattices in industrial applications, including blending, warping, field-directed distributions, region-specific cell types, and produces arbitrary directional slicing for manufacturing. As demonstrated by various examples implemented in WebGPU, AST archives high efficiency and accuracy in real-time rendering of shell-lattices with billions of beams on an RTX 3090, outperforming traditional pipelines in storage, frame time, and detail preservation.

关键词： Lattice structures Real-time rendering Hybrid explicit-implicit Mega-scale Additive manufacturing

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Physically reliable 3D styled shape generation via structure-aware topology optimization in unified latent space

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COMPUTER-AIDED DESIGN 2025年 183卷

作者： Ijaz, Haroon Wang, Xuwei Chen, Wei Lin, Hai Li, Ming Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Peoples R China

We propose a novel approach to structure-aware topology optimization (SATO) to generate physically plausible multi-component structures with diverse stylistic variations. Traditional TO methods often operate within a discrete voxel-defined design space, overlooking the underlying structure-aware, which limits their ability to accommodate stylistic design preferences. Our approach leverages variational autoencoders (VAEs) to encode both geometries and corresponding structures into a unified latent space, capturing part arrangement features. The design target is carefully formulated as a topology optimization problem taking the VAE code as design variables under physical constraints, and solved numerically via analyzing the associated sensitivity with respect to the VAE variables. Our numerical examples demonstrate the ability to generate lightweight structures that balance geometric plausibility and structural performance with much enhanced stiffness that outperforms existing generative techniques. The method also enables the generation of diverse and reliable designs, maintaining structural integrity throughout, via a direct smooth interpolation between the optimized designs. The findings highlight the potential of our approach to bridge the gap between generative design and physics-based optimization by incorporating deep learning techniques.

关键词： Generative design 3D shape creation Topology optimization Variational autoencoder Finite element analysis Global convergent MMA

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Geometric Modeling for Microstructure Design and Manufacturing: A Review of Representations and Modeling Algorithms

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COMPUTER-AIDED DESIGN 2025年 180卷

作者： Zou, Qiang Luo, Guoyue Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Peoples R China

Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental technique of microstructure design and manufacturing is geometric modeling, which generates the 3D computer models required to run high-level procedures such as simulation, optimization, and process planning. There is, however, a lack of comprehensive discussions on this body of knowledge. The goal of this paper is to compile existing microstructure modeling methods and clarify the challenges, progress, and limitations of current research. It also concludes with future research directions that may improve and/or complement current methods, such as compressive and generative microstructure representations. By doing so, the paper sheds light on what has already been made possible for microstructure modeling, what developments can be expected in the near future, and which topics remain problematic.

关键词： cad/CAM Additive manufacturing Geometric modeling Microstructures Geometric representations Modeling algorithms

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SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 3591-3601页

作者： Zhai, Hongjia Zhang, Xiyu Zhao, Boming Li, Hai He, Yijia Cui, Zhaopeng Bao, Hujun Zhang, Guofeng Zhejiang University State Key Lab of CAD&CG Hangzhou310058 China RayNeo China

Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Code and data are available at project page: https://***/splatloc. © 1995-2012 IEEE.

关键词： Augmented reality

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SplineGen: Approximating unorganized points through generative AI

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COMPUTER-AIDED DESIGN 2025年 178卷

作者： Zou, Qiang Zhu, Lizhen Wu, Jiayu Yang, Zhijie Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Zhejiang Peoples R China

This paper presents a learning-based method to solve the traditional parameterization and knot placement problems in B-spline approximation. Different from conventional heuristic methods or recent AI-based methods, the proposed method does not assume ordered or fixed-size data points as input. There is also no need for manually setting the number of knots. Parameters and knots are generated in an associative way to attain better parameter-knot alignment, and therefore a higher approximation accuracy. These features are attained by using anew generative model SplineGen, which casts the parameterization and knot placement problems as a sequence-to-sequence translation problem. It first adopts a shared autoencoder model to learn a 512-D embedding for each input point, which has the local neighborhood information implicitly captured. Then these embeddings are autoregressively decoded into parameters and knots by two associative decoders, a generative process automatically determining the number of knots, their placement, parameter values, and their ordering. The two decoders are made to work in a coordinated manner by a new network module called internal cross- attention. Once trained, SplineGen demonstrates a notable improvement over existing methods, with one to two orders of magnitude increase in approximation accuracy on test data.

关键词： cad B-spline Approximation Parameterization Knot placement Generative modeling

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AutoStory: Generating Diverse Storytelling Images with Minimal Human Efforts

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第6期133卷 3083-3104页

作者： Wang, Wen Zhao, Canyu Chen, Hao Chen, Zhekai Zheng, Kecheng Shen, Chunhua Zhejiang Univ Hangzhou Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China

Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches, and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images. This allows our method to obtain consistent story visualization even when only texts are provided as input. Both qualitative and quantitative experiments demonstrate the superiority of our method.

关键词： Generative models Machine learning Diffusion models Low-rank adaptation

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Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2025年第1期31卷 1-11页

作者： Cao, Anqi Xie, Xiao Zhang, Runjin Tian, Yuxin Fan, Mu Zhang, Hui Wu, Yingcai Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Zhejiang Univ Dept Sports Sci Hangzhou Peoples R China

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

关键词： Soccer Visualization Player Scouting Design Study

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Image-aware layout generation with user constraints for poster design

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VISUAL COMPUTER 2025年第6期41卷 4239-4252页

作者： Xu, Chenchen Han, Kaixin Xu, Weiwei Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

Graphic layout is essential in poster generation. Professionals often need to design different layouts for a product image, to ensure they meet specific user requirements. This paper focuses on utilizing a deep-learning model to automatically generate image-aware layouts with user-defined constraints, including layout attributes and partial layouts. Layout attribute constraints require generated layouts to include and exclude elements of specified classes, such as text, logos, underlays, and embellishments. Our model represents different attributes by sampling multidimensional Gaussian noise with different means, and we propose an attribute-consistent loss and an attribute-disentangled loss to ensure that the generated layout satisfies the specified attribute. Partial layout constraints provide our model with incomplete layout information to guide the generation of the remaining elements. We design a partial-constraint loss to incorporate the provided partial layout. Furthermore, we introduce a random mask to diversify the partial layout constraints, which can encourage the model to learn more general latent representations of the provided partial layouts. Both quantitative and qualitative evaluations demonstrate that our model can generate different image-aware layouts according to various user constraints while achieving state-of-the-art performance.

关键词： Graphic layout Poster Image-aware User constraints Layout attributes Partial layouts

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Persistent homology-driven optimization of effective relative density range for triply periodic minimal surfaces

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COMPUTER-AIDED DESIGN 2025年 180卷

作者： Gao, Depeng Zhang, Yuanzhi Lin, Hongwei Zou, Qiang Zhejiang Univ Sch Math Sci Hangzhou 310058 Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China

Triply periodic minimal surfaces (TPMSs) playa vital role in the design of porous structures, with applications in bone tissue engineering, chemical engineering, and the creation of lightweight models. However, fabrication of TPMSs via additive manufacturing is feasible only within a specific range of relative densities, termed the effective relative density range (EDR), outside of which TPMSs exhibit unmanufacturable features. In this study, the persistent homology is applied to theoretically calculate and extend the EDRs of TPMSs. To expand the design domain of TPMSs while maintaining their symmetry, TPMSs are transformed into a new representation based on B-spline functions and a series of operators, which is referred to as GeneralizedTPMSs. Through topological analysis, the Generalized-TPMSs are further optimized to expand their EDRs while maintaining a high degree of similarity to the original TPMSs. The optimized Generalized-TPMSs are referred to as EDR-Variant TPMS (EDR-VTPMS). Experimental validation confirms the effectiveness of the approach in extending the EDRs of TPMSs. Moreover, EDR-VTPMS exhibits enhanced performance in heterogeneous and high-stiffness model design compared to their conventional counterparts. The EDR-VTPMSs with increased EDRs hold promise for replacing traditional TPMSs in applications that require porous structures with varying densities.

关键词： Triply periodic minimal surfaces (TPMSs) Persistent homology Porous model design Implicit B-spline representation

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