用连续神经表示替代传统离散纹理，提升图像质量与渲染效率。
Implicit neural representation of textures
- 设计连续空间的神经纹理表示，基于输入UV坐标直接生成像素。
- 在保持高图像质量的同时，内存占用和渲染时间显著降低。
- 适用于实时渲染、多级细节图生成等下游任务，适合图形学研究者。
隐式神经表示（INR）已在多个领域证明其精度与效率。本文探索如何设计不同神经网络作为新型纹理隐式表示，该表示在输入的UV坐标空间中以连续方式运行，而非传统的离散方式。通过全面实验，我们证明这些INR在图像质量方面表现优异，同时具备较低的内存占用和较快的渲染推理速度。我们分析了图像质量、内存使用与推理时间之间的权衡关系。此外，我们还研究了其在实时渲染及下游任务中的应用，例如多级细节图（mipmap）拟合与INR空间生成。
原文摘要 · Abstract (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.
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