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Item The Use of Photogrammetry in Historic Preservation Curriculum: A Comparative Case Study(The Eurographics Association, 2024) Kepczynska-Walczak, Anetta; Walczak, Bartosz M.; Zarzycki, Andrzej; Sousa Santos, Beatriz; Anderson, EikeComputer graphic techniques have emerged as a key player in digital heritage preservation and its dissemination. Photogrammetry allows for high-fidelity captures and virtual reconstructions of the built environment that can be further ported into virtual reality (VR) and augmented reality (AR) experiences. This paper provides a comparative analysis of historic details and building documentation methods in heritage preservation in the context of architectural education. Specifically, it compares two educational case studies conducted in 10-year intervals documenting the same set of historic artifacts with corresponding state-of-the-art digital technologies. The methodology for this paper is a qualitative comparative analysis of two surveying projects that utilized distinct emerging digital technology while sharing the same study subjects and similar tool-driven curricular framework. The research also incorporates a student survey, offering perspectives on teaching strategies and outcomes within this dynamic educational context. The outcomes demonstrate that the technological (tool-driven) shift impacts the way students interact with the investigated artifacts and the changing role of the interpretative versus analytical skills needed to delineate the work. It also changes what are considered primary and secondary knowledge sources.Item Real-time Seamless Object Space Shading(The Eurographics Association, 2024) Li, Tianyu; Guo, Xiaoxin; Hu, Ruizhen; Charalambous, PanayiotisObject space shading remains a challenging problem in real-time rendering due to runtime overhead and object parameterization limitations. While the recently developed algorithm by Baker et al. [BJ22] enables high-performance real-time object space shading, it still suffers from seam artifacts. In this paper, we introduce an innovative object space shading system leveraging a virtualized per-halfedge texturing schema to obviate excessive shading and preclude texture seam artifacts. Moreover, we implement ReSTIR GI on our system (see Figure 1), removing the necessity of temporally reprojecting shading samples and improving the convergence of areas of disocclusion. Our system yields superior results in terms of both efficiency and visual fidelity.Item Efficient Needle Insertion Simulation using Hybrid Constraint Solver and Isolated DOFs(The Eurographics Association, 2023) Martin, Claire; Zeng, Ziqiu; Courtecuisse, Hadrien; Babaei, Vahid; Skouras, MelinaThis paper introduces a real-time compatible method to improve the location of constraints between a needle and tissues in the context of needle insertion simulation. This method is based on intersections between the Finite Element (FE) meshes of the needle and the tissues. It is coupled with the method of isolating mechanical DOFs and a hybrid solver (implying both direct and iterative resolutions) to respectively generate and solve the constraint problem while reducing the computation time.Item Velocity-Based LOD Reduction in Virtual Reality: A Psychophysical Approach(The Eurographics Association, 2023) Petrescu, David; Warren, Paul A.; Montazeri, Zahra; Pettifer, Steve; Babaei, Vahid; Skouras, MelinaVirtual Reality headsets enable users to explore the environment by performing self-induced movements. The retinal velocity produced by such motion reduces the visual system's ability to resolve fine detail. We measured the impact of self-induced head rotations on the ability to detect quality changes of a realistic 3D model in an immersive virtual reality environment. We varied the Level of Detail (LOD) as a function of rotational head velocity with different degrees of severity. Using a psychophysical method, we asked 17 participants to identify which of the two presented intervals contained the higher quality model under two different maximum velocity conditions. After fitting psychometric functions to data relating the percentage of correct responses to the aggressiveness of LOD manipulations, we identified the threshold severity for which participants could reliably (75%) detect the lower LOD model. Participants accepted an approximately four-fold LOD reduction even in the low maximum velocity condition without a significant impact on perceived quality, suggesting that there is considerable potential for optimisation when users are moving (increased range of perceptual uncertainty). Moreover, LOD could be degraded significantly more (around 84%) in the maximum head velocity condition, suggesting these effects are indeed speed-dependent.Item DeepIron: Predicting Unwarped Garment Texture from a Single Image(The Eurographics Association, 2024) Kwon, Hyun-Song; Lee, Sung-Hee; Hu, Ruizhen; Charalambous, PanayiotisRealistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single garment image with pose. Since 3D garments are effectively modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input garment image, which exhibits warping and occlusion of the garment due to the user's body shape and pose. This is effectively achieved by translating between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies.Item Automatic Step Size Relaxation in Sphere Tracing(The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Babaei, Vahid; Skouras, MelinaWe propose a robust auto-relaxed sphere tracing method that automatically scales its step sizes based on data from previous iterations. It possesses a scalar hyperparemeter that is used similarly to the learning rate of gradient descent methods. We show empirically that this scalar degree of freedom has a smaller effect on performance than the step-scale hyperparameters of concurrent sphere tracing variants. Additionally, we compare the performance of our algorithm to these both on procedural and discrete signed distance input and show that it outperforms or performs up to par to the most efficient method, depending on the limit on iteration counts. We also verify that our method takes significantly fewer robustness-preserving sphere trace fallback steps, as it generates fewer invalid, over-relaxed step sizes.Item Guiding Light Trees for Many-Light Direct Illumination(The Eurographics Association, 2023) Hamann, Eric; Jung, Alisa; Dachsbacher, Carsten; Babaei, Vahid; Skouras, MelinaPath guiding techniques reduce the variance in path tracing by reusing knowledge from previous samples to build adaptive sampling distributions. The Practical Path Guiding (PPG) approach stores and iteratively refines an approximation of the incident radiance field in a spatio-directional data structure that allows sampling the incident radiance. However, due to the limited resolution in both spatial and directional dimensions, this discrete approximation is not able to accurately capture a large number of very small lights. We present an emitter sampling technique to guide next event estimation (NEE) with a global light tree and adaptive tree cuts that integrates into the PPG framework. In scenes with many lights our technique significantly reduces the RMSE compared to PPG with uniform NEE, while adding close to no overhead in scenes with few light sources. The results show that our technique can also aid the incident radiance learning of PPG in scenes with difficult visibility.Item Predictive Modeling of Material Appearance: From the Drawing Board to Interdisciplinary Applications(The Eurographics Association, 2024) Baranoski, Gladimir V. G.; Mania, Katerina; Artusi, AlessandroThis tutorial addresses one of the fundamental and timely topics of computer graphics research, namely the predictive modeling of material appearance. Although this topic is deeply rooted in traditional areas like rendering and natural phenomena simulation, this tutorial is not limited to cover contents connected to these areas. It also closely looks into the scientific methodology employed in the development of predictive models of light and matter interactions. Given the widespread use of this methodology to find modeling solutions for problems within and outside computer graphics, its discussion from a ''behind the scenes'' perspective aims to underscore practical and far-reaching aspects of interdisciplinary research that are often overlooked in related publications. More specifically, this tutorial unveils constraints and pitfalls found in each of the key stages of the model development process, namely data collection, design and evaluation, and brings forward alternatives to tackle them effectively. Furthermore, besides being a central component of realistic image synthesis frameworks, predictive material appearance models have a scope of applications that can be extended far beyond the generation of believable images. For instance, they can be employed to accelerate the hypothesis generation and validation cycles of research across a wide range of fields, from biology and medicine to photonics and remote sensing, among others. These models can also be used to generate comprehensive in silico (computational) datasets to support the translation of knowledge advances in those fields to real-world applications (e.g., the noninvasive screening of medical conditions and the remote detection of environmental hazards). In fact, a number of them are already being used in physical and life sciences, notably to support investigations seeking to strengthen the current understanding about material appearance changes prompted by mechanisms which cannot be fully studied using standard ''wet'' experimental procedures. Accordingly, such interdisciplinary research initiatives are also discussed in this tutorial through selected case studies involving the use of predictive material appearance models to elucidate challenging scientific questions.Item Quick-Pro-Build: A Web-based Approach for Quick Procedural 3D Reconstructions of Buildings(The Eurographics Association, 2023) Bohlender, Bela; Mühlhäuser, Max; Guinea, Alejandro Sanchez; Babaei, Vahid; Skouras, MelinaWe present Quick-Pro-Build, a web-based approach for quick procedural 3D reconstruction of buildings. Our approach allows users to quickly and easily create realistic 3D models using two integrated reference views: street view and satellite view. We introduce a novel conditional and stochastic shape grammar to represent the procedural models based on the well-established CGA shape grammar. Based on our grammar and user interface, we propose 3 modalities for procedural modeling: 1) model from scratch, 2) copy, paste, and adapt, and 3) summarize, select and adapt. The third modality enables users to model a building by summarizing similar models into an architectural style description, selecting a model from the style description, and adapting it to the target building. Summarizing and selecting allows the third modality to be the most efficient option when modeling a building with a style similar to existing buildings. The third modality is enabled by a novel algorithm that can find and combine similarities from procedural models into a style description and allows learning the preference of the users for one model inside the style description.Item Dense 3D Gaussian Splatting Initialization for Sparse Image Data(The Eurographics Association, 2024) Seibt, Simon; Chang, Thomas Vincent Siu-Lung; von Rymon Lipinski, Bartosz ; Latoschik, Marc Erich; Liu, Lingjie; Averkiou, MelinosThis paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving ''Dense Feature Matching for Structure-from-Motion'' (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a welldistributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence.