Improved Stochastic Texture Filtering Through Sample Reuse

• URL: http://arxiv.org/abs/2504.05562v1
• Date: Mon, 07 Apr 2025 23:28:52 GMT
• Title: Improved Stochastic Texture Filtering Through Sample Reuse
• Authors: Bartlomiej Wronski, Matt Pharr, Tomas Akenine-Möller,
• Abstract summary: texture filtering (STF) has re-emerged as a technique that can bring down the cost of texture filtering of advanced texture compression methods.<n>During texture magnification, the swapped order of filtering and shading with STF can result in aliasing.<n>We present a novel method to improve the quality of textureally-filtered magnified textures and reduce the image difference.
• Score: 1.9608359347635143
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Stochastic texture filtering (STF) has re-emerged as a technique that can bring down the cost of texture filtering of advanced texture compression methods, e.g., neural texture compression. However, during texture magnification, the swapped order of filtering and shading with STF can result in aliasing. The inability to smoothly interpolate material properties stored in textures, such as surface normals, leads to potentially undesirable appearance changes. We present a novel method to improve the quality of stochastically-filtered magnified textures and reduce the image difference compared to traditional texture filtering. When textures are magnified, nearby pixels filter similar sets of texels and we introduce techniques for sharing texel values among pixels with only a small increase in cost (0.04--0.14~ms per frame). We propose an improvement to weighted importance sampling that guarantees that our method never increases error beyond single-sample stochastic texture filtering. Under high magnification, our method has >10 dB higher PSNR than single-sample STF. Our results show greatly improved image quality both with and without spatiotemporal denoising.

Related papers

• NeRF-Texture: Synthesizing Neural Radiance Field Textures [77.24205024987414]
  We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images.<n>In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape.<n>We can synthesize NeRF-based textures through patch matching of latent features.
  arXiv  Detail & Related papers  (2024-12-13T09:41:48Z)
• Filtering After Shading With Stochastic Texture Filtering [1.8377890861896995]
  We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering before BSDF evaluation. texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures.
  arXiv  Detail & Related papers  (2024-05-14T16:42:07Z)
• Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis [61.189479577198846]
  We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU.
  arXiv  Detail & Related papers  (2024-05-13T21:53:09Z)
• Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering [47.78392889256976]
  Paint-it is a text-driven high-fidelity texture map synthesis method for 3D rendering. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS) We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS.
  arXiv  Detail & Related papers  (2023-12-18T17:17:08Z)
• Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
  Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss. We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
  arXiv  Detail & Related papers  (2023-07-09T08:29:19Z)
• Lightweight texture transfer based on texture feature preset [1.1863107884314108]
  We propose a lightweight texture transfer based on texture feature preset. The results show visually superior results but also reduces the model size by 3.2-3538 times and speeds up the process by 1.8-5.6 times.
  arXiv  Detail & Related papers  (2023-06-29T10:37:29Z)
• Pyramid Texture Filtering [86.15126028139736]
  We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts.
  arXiv  Detail & Related papers  (2023-05-11T02:05:30Z)
• Stochastic Texture Filtering [3.4202659118354104]
  Filtered texture lookups are integral to producing high-quality imagery. We show that filtering after evaluating lighting, rather than before BSDF evaluation as is current practice, gives a more accurate solution to the rendering equation. We demonstrate applications in both real-time and offline rendering and show that the additional error is minimal.
  arXiv  Detail & Related papers  (2023-05-09T23:50:25Z)
• MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting [35.79101039727397]
  We study the advantages and challenges of image-level predictive filtering for image inpainting. We propose a novel filtering technique, i.e., Multilevel Interactive Siamese Filtering (MISF), which contains two branches: kernel prediction branch (KPB) and semantic & image filtering branch (SIFB) Our method outperforms state-of-the-art baselines on four metrics, i.e., L1, PSNR, SSIM, and LPIPS.
  arXiv  Detail & Related papers  (2022-03-12T01:32:39Z)
• Adaptive Debanding Filter [55.42929350861115]
  Banding artifacts manifest as staircase-like color bands on pictures or video frames. We propose a content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module. Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.
  arXiv  Detail & Related papers  (2020-09-22T20:44:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.