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.

翻译：隐式神经表示（INR）已在多个领域被证明是准确且高效的。在本工作中，我们探讨了如何将不同的神经网络设计为一种新的纹理INR，该表示在输入UV坐标空间上以连续而非离散的方式运行。通过详尽的实验，我们证明这些INR在图像质量方面表现良好，同时具有可观的内存使用量和渲染推理时间。我们分析了这些目标之间的平衡关系。此外，我们研究了在实时渲染及下游任务（例如mipmap拟合与INR空间生成）中的多种相关应用。