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Now showing 1 - 10 of 19
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    Selective Caching in Procedural Texture Graphs for Path Tracing
    (The Eurographics Association, 2025) Schüßler, Vincent; Hanika, Johannes; Sauvage, Basile; Dischler, Jean-Michel; Dachsbacher, Carsten; Wang, Beibei; Wilkie, Alexander
    Procedural texturing is crucial for adding details in large-scale rendering. Typically, procedural textures are represented as computational graphs that artists can edit. However, as scene and graph complexity grow, evaluating these graphs becomes increasingly expensive for the rendering system. Performance is greatly affected by the evaluation strategy: Precomputing textures into high resolution maps is straightforward but can be inefficient, while shade-on-hit architectures and tile-based caches improve efficiency by evaluating only necessary data. However, the ideal choice of strategy depends on the application context. We present a new method to dynamically select which texture graph nodes to cache within a rendering system that supports filtered texture graph evaluation and tile-based caching. Our method allows us to construct an optimized evaluation strategy for each scene. Cache-friendly nodes are identified using data-driven predictions based on statistics of requested texture footprints, gathered during a profiling phase. We develop a statistical model that fits profiling data and predicts how caching specific nodes affects evaluation efficiency and storage demands. Our approach can be directly integrated into a rendering system or used to analyze renderer data, helping practitioners to optimize performance in their workflows.
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    Efficient Modeling and Rendering of Iridescence from Cholesteric Liquid Crystals
    (The Eurographics Association, 2025) Fourneau, Gary; Barla, Pascal; Pacanowski, Romain; Wang, Beibei; Wilkie, Alexander
    We introduce a novel approach to the efficient modeling and rendering of Cholesteric Liquid Crystals (CLCs), materials known for producing colorful effects due to their helical molecular structure. CLCs reflect circularly-polarized light within specific spectral bands, making their accurate simulation challenging for realistic rendering in Computer Graphics. Using the two-wave approximation from the Photonics literature, we develop a piecewise spectral reflectance model that improves the understanding of how light interact with CLCs for arbitrary incident angles. Our reflectance model allows for more efficient spectral rendering and fast integration into RGB-based rendering engines. We show that our approach is able to reproduce the unique visual properties of both natural and man-made CLCs, while keeping the computation fast enough for interactive applications and avoiding potential spectral aliasing issues.
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    Differentiable Block Compression for Neural Texture
    (The Eurographics Association, 2025) Zhuang, Tao; Liu, Wentao; Liu, Ligang; Wang, Beibei; Wilkie, Alexander
    In real-time rendering, neural network models using neural textures (texture-form neural features) are increasingly applied. For high-memory scenarios like film-grade games, reducing neural texture memory overhead is critical. While neural textures can use hardware-accelerated block compression for memory savings and leverage hardware texture filtering for performance, mainstream block compression encoders only aim to minimize compression errors. This design may significantly increase neural network model loss.We propose a novel differentiable block compression (DBC) framework that integrates encoding and decoding into neural network optimization training. Compared with direct compression by mainstream encoders, end-to-end trained neural textures reduce model loss. The framework first enables differentiable encoding computation, then uses a compressionerror- based stochastic sampling strategy for encoding configuration selection. A Mixture of Partitions (MoP) module is introduced to reduce computational costs from multiple partition configurations. As DBC employs native block compression formats, inference maintains real-time performance.
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    Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning
    (The Eurographics Association, 2025) Gu, Yi; Wang, Zhaorui; Xu, Renjing; Wang, Beibei; Wilkie, Alexander
    Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering. Extracting geometry from NeRF typically relies on the Marching Cubes algorithm, which uses a hand-crafted threshold to define the level set. However, this threshold-based approach requires laborious and scenario-specific tuning, limiting its practicality for real-world applications. In this work, we seek to enhance the efficiency of this method during the training time. To this end, we introduce a spiking neuron mechanism that dynamically adjusts the threshold, eliminating the need for manual selection. Despite its promise, directly training with the spiking neuron often results in model collapse and noisy outputs. To overcome these challenges, we propose a round-robin strategy that stabilizes the training process and enables the geometry network to achieve a sharper and more precise density distribution with minimal computational overhead. We validate our approach through extensive experiments on both synthetic and real-world datasets. The results show that our method significantly improves the performance of threshold-based techniques, offering a more robust and efficient solution for NeRF geometry extraction.
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    Radiative Backpropagation with Non-Static Geometry
    (The Eurographics Association, 2025) Worchel, Markus; Finnendahl, Ugo; Alexa, Marc; Wang, Beibei; Wilkie, Alexander
    Radiative backpropagation-based (RB) methods efficiently compute reverse-mode derivatives in physically-based differentiable rendering by simulating the propagation of differential radiance. A key assumption is that differential radiance is transported like normal radiance. We observe that this holds only when scene geometry is static and demonstrate that current implementations of radiative backpropagation produce biased gradients when scene parameters change geometry. In this work, we derive the differential transport equation without assuming static geometry. An immediate consequence is that the parameterization matters when the sampling process is not differentiated: only surface integrals allow a local formulation of the derivatives, i.e., one in which moving surfaces do not affect the entire path geometry. While considerable effort has been devoted to handling discontinuities resulting from moving geometry, we show that a biased interior derivative compromises even the simplest inverse rendering tasks, regardless of discontinuities. An implementation based on our derivation leads to systematic convergence to the reference solution in the same setting and provides unbiased RB interior derivatives for path-space differentiable rendering.
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    A Controllable Appearance Representation for Flexible Transfer and Editing
    (The Eurographics Association, 2025) Jimenez-Navarro, Santiago; Guerrero-Viu, Julia; Masia, Belen; Wang, Beibei; Wilkie, Alexander
    We present a method that computes an interpretable representation of material appearance within a highly compact, disentangled latent space. This representation is learned in a self-supervised fashion using a VAE-based model. We train our model with a carefully designed unlabeled dataset, avoiding possible biases induced by human-generated labels. Our model demonstrates strong disentanglement and interpretability by effectively encoding material appearance and illumination, despite the absence of explicit supervision. To showcase the capabilities of such a representation, we leverage it for two proof-of-concept applications: image-based appearance transfer and editing. Our representation is used to condition a diffusion pipeline that transfers the appearance of one or more images onto a target geometry, and allows the user to further edit the resulting appearance. This approach offers fine-grained control over the generated results: thanks to the well-structured compact latent space, users can intuitively manipulate attributes such as hue or glossiness in image space to achieve the desired final appearance.
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    Procedural Bump-based Defect Synthesis for Industrial Inspection
    (The Eurographics Association, 2025) Mao, Runzhou; Garth, Christoph; Gospodnetic, Petra; Wang, Beibei; Wilkie, Alexander
    Automated defect detection is critical for quality control, but collecting and annotating real-world defect images remains costly and time-consuming, motivating the use of synthetic data. Existing methods such as geometry-based modeling, normal maps, and image-based approaches often struggle to balance realism, efficiency, and scalability. We propose a procedural method for synthesizing small-scale surface defects using gradient-based bump mapping and triplanar projection. By perturbing surface normals at shading time, our approach enables parameterized control over diverse scratch and dent patterns, while avoiding mesh edits, UV mapping, or texture lookup. It also produces pixel-accurate defect masks for annotation. Experimental results show that our method achieves comparable visual quality to geometry-based modeling, with lower computational overhead and improved surface continuity over static normal maps. The method offers a lightweight and scalable solution for generating high-quality training data for industrial inspection tasks.
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    Uncertainty-Aware Gaussian Splatting with View-Dependent Regularization for High-Fidelity 3D Reconstruction
    (The Eurographics Association, 2025) Liu, Shengjun; Wu, Jiangxin; Wu, Wenhui; Chu, Lixiang; Liu, Xinru; Wang, Beibei; Wilkie, Alexander
    3D Gaussian Splatting (3DGS) has emerged as a groundbreaking paradigm for explicit scene representation, achieving photorealistic novel view synthesis with real-time rendering capabilities. However, reconstructing geometrically consistent and accurate surfaces under complex real-world scenarios remains a critical challenge. Current 3DGS frameworks primarily rely on photometric loss optimization, which often results in multi-view geometric inconsistencies and inadequate handling of texture-less regions due to two inherent limitations: 1) the absence of explicit geometric constraints during Gaussian parameter optimization, and 2) the lack of effective mechanisms to resolve multi-view geometric ambiguities. To address these challenges, we propose Uncertainty-Aware Gaussian Splatting (UA-GS), a novel framework that integrates geometric priors with view-dependent uncertainty modeling to explicitly capture and resolve multi-view inconsistencies. For efficient optimization of Gaussian attributes, our approach introduces a spherical harmonics-based uncertainty representation that dynamically models view-dependent geometric variations. Building on this framework, we further design uncertainty-aware optimization strategies. Extensive experiments on real-world and synthetic benchmarks demonstrate that our method significantly outperforms state-of-the-art 3DGS-based approaches in geometric accuracy while retaining competitive rendering quality. Code and data will be made available soon.
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    Iterative Nonparametric Bayesian CP Decomposition for Hyperspectral Image Denoising
    (The Eurographics Association, 2025) Liu, Wei; Jiang, Kaiwen; Lai, Jinzhi; Zhang, Xuesong; Wang, Beibei; Wilkie, Alexander
    Hyperspectral image (HSI) denoising relies on exploiting the multiway correlations hidden in the clean signals to discriminate between the randomness of measurement noise. This paper proposes a self-supervised model that has a three-layer algorithmic hierarchy to iteratively quest for the tensor decomposition based representation of the underlying HSI. The outer layer takes advantage of the non-local similarity of HSI via a simple but effective k-means++ algorithm to explore the patch-level correlation and yields clusters of patches with similar tensor ranks. The middle and inner layers consist of a Bayesian Nonparametric tensor decomposition framework. The middle one employs a multiplicative Gamma process prior for the low rank tensor decomposition, and a Gaussian-Wishart prior for a more flexible exploration of the correlations among the latent factor matrices. The inner layer is responsible for the finer regression of the residual multiway correlations leaked from the upper two layers. Our scheme also provides a principled and automatic solution to several practical HSI denoising factors, such as the noise level, the model complexity and the regularization weights. Extensive experiments validate that our method outperforms state-of-the-art methods on a series of HSI denoising metrics.
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    From Optical Measurement to Visual Comfort Analysis: a Complete Simulation Workflow with Oceanâ„¢'s Glare Map Post-processing
    (The Eurographics Association, 2025) Bandeliuk, Oleksandra; Besse, Grégoire; Pierrard, Thomas; Berthier, Estelle; Wang, Beibei; Wilkie, Alexander
    Lighting critically influences public safety and visual comfort across environments. Discomfort glare, in particular, poses a major challenge. We here introduce Oceanâ„¢'s glare map, a fast, high-fidelity glare evaluation tool that computes key indices (UGR, DGP, GR) through post-processing of spectral global illumination simulations. Beyond whole-scene assessments, our glare map tool uniquely offers per-source glare ratings, enabling precise design optimization. Through three practical use cases, we demonstrate the effectiveness of our tool for operational design and show how changes in illumination and material properties directly affect glare, supporting safer and more efficient lighting designs.