纹理的隐式神经表示

摘要

隱式神經表示（INR）在多個領域已被證實具有精確高效的特性。本研究探討如何將不同神經網絡設計為新型紋理INR，該表示在輸入的UV座標空間中採用連續運算模式而非離散方式。透過系統性實驗，我們證明這些INR在影像質量方面表現優異，同時具備可觀的記憶體使用效率與渲染推理速度。本文深入分析了這些性能指標間的平衡關係，並進一步探索了在即時渲染與下游任務中的多種相關應用，例如mipmap擬合與INR空間生成。

English

Implicit neural representation (INR) has proven to be accurate and efficient in various domains. In this work, we explore how different neural networks can be designed as a new texture INR, which operates in a continuous manner rather than a discrete one over the input UV coordinate space. Through thorough experiments, we demonstrate that these INRs perform well in terms of image quality, with considerable memory usage and rendering inference time. We analyze the balance between these objectives. In addition, we investigate various related applications in real-time rendering and down-stream tasks, e.g. mipmap fitting and INR-space generation.

纹理的隐式神经表示

Implicit neural representation of textures

摘要

Support