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Item 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, AlexanderProcedural 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.Item Efficient Modeling and Rendering of Iridescence from Cholesteric Liquid Crystals(The Eurographics Association, 2025) Fourneau, Gary; Barla, Pascal; Pacanowski, Romain; Wang, Beibei; Wilkie, AlexanderWe 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.Item Differentiable Block Compression for Neural Texture(The Eurographics Association, 2025) Zhuang, Tao; Liu, Wentao; Liu, Ligang; Wang, Beibei; Wilkie, AlexanderIn 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.Item Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning(The Eurographics Association, 2025) Gu, Yi; Wang, Zhaorui; Xu, Renjing; Wang, Beibei; Wilkie, AlexanderNeural 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.Item Radiative Backpropagation with Non-Static Geometry(The Eurographics Association, 2025) Worchel, Markus; Finnendahl, Ugo; Alexa, Marc; Wang, Beibei; Wilkie, AlexanderRadiative 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.Item A Controllable Appearance Representation for Flexible Transfer and Editing(The Eurographics Association, 2025) Jimenez-Navarro, Santiago; Guerrero-Viu, Julia; Masia, Belen; Wang, Beibei; Wilkie, AlexanderWe 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.Item Procedural Bump-based Defect Synthesis for Industrial Inspection(The Eurographics Association, 2025) Mao, Runzhou; Garth, Christoph; Gospodnetic, Petra; Wang, Beibei; Wilkie, AlexanderAutomated 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.Item Content-Aware Texturing for Gaussian Splatting(The Eurographics Association, 2025) Papantonakis, Panagiotis; Kopanas, Georgios; Durand, Frédo; Drettakis, George; Wang, Beibei; Wilkie, AlexanderGaussian Splatting has become the method of choice for 3D reconstruction and real-time rendering of captured real scenes. However, fine appearance details need to be represented as a large number of small Gaussian primitives, which can be wasteful when geometry and appearance exhibit different frequency characteristics. Inspired by the long tradition of texture mapping, we propose to use texture to represent detailed appearance where possible. Our main focus is to incorporate per-primitive texture maps that adapt to the scene in a principled manner during Gaussian Splatting optimization. We do this by proposing a new appearance representation for 2D Gaussian primitives with textures where the size of a texel is bounded by the image sampling frequency and adapted to the content of the input images. We achieve this by adaptively upscaling or downscaling the texture resolution during optimization. In addition, our approach enables control of the number of primitives during optimization based on texture resolution. We show that our approach performs favorably in image quality and total number of parameters used compared to alternative solutions for textured Gaussian primitives.Item 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, Alexander3D 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.Item Stochastic Ray Tracing of Transparent 3D Gaussians(The Eurographics Association, 2025) Sun, Xin; Georgiev, Iliyan; Fei, Yun (Raymond); Hasan, Milos; Wang, Beibei; Wilkie, Alexander3D Gaussian splatting has been widely adopted as a 3D representation for novel-view synthesis, relighting, and 3D generation tasks. It delivers realistic and detailed results through a collection of explicit 3D Gaussian primitives, each carrying opacity and view-dependent color. However, efficient rendering of many transparent primitives remains a significant challenge. Existing approaches either rasterize the Gaussians with approximate per-view sorting or rely on high-end RTX GPUs. This paper proposes a stochastic ray-tracing method to render 3D clouds of transparent primitives. Instead of processing all ray-Gaussian intersections in sequential order, each ray traverses the acceleration structure only once, randomly accepting and shading a single intersection (or N intersections, using a simple extension). This approach minimizes shading time and avoids primitive sorting along the ray, thereby minimizing register usage and maximizing parallelism even on low-end GPUs. The cost of rays through the Gaussian asset is comparable to that of standard mesh-intersection rays. The shading is unbiased and has low variance, as our stochastic acceptance achieves importance sampling based on accumulated weight. The alignment with Monte Carlo philosophy simplifies implementation and integration into a conventional path-tracing framework.