AI 中文总结
该研究提出GTC方法，通过共享2D高斯函数压缩带多级纹理滤波的SVBRDF纹理，相比ASTC实现更高重建质量与更低内存，支持随机访问和非神经解码，适用于实时渲染
AI 中文摘要
空间变化双向反射分布函数（SVBRDF）是计算机图形学中材质表示的核心，但其高分辨率、多通道、带多级纹理滤波（mipmapped）的纹理会带来巨大的存储负担。现有压缩方法存在根本权衡：基于块的压缩支持随机访问和硬件友好的解码，但仅能利用局部块内的冗余；图像编解码器具有优异的率失真性能，但并非为直接实时纹理访问设计；神经纹理压缩实现了高压缩率，但解码时需要神经推理，会引入额外运行时开销，尤其在移动平台上。我们提出高斯纹理压缩（Gaussian Texture Compression, GTC），这是一种用于带多级纹理滤波的SVBRDF纹理栈的基于2D高斯函数的紧凑表示，可实现高质量压缩并提供灵活的率失真权衡。我们的方法基于一个关键观察：这类数据中存在两类主要冗余源，即跨多级纹理滤波层级和跨材质贴图，两者共享共同的底层结构：相同的空间支撑域被重复使用，仅附加层级或贴图特定的信息。该特性非常适合2D高斯函数，因为每个高斯函数明确将其空间支撑域与携带的值分离，允许支撑域被共享，而值随层级和贴图变化。基于此特性，GTC沿两类冗余维度共享高斯函数，并通过渐进式优化流程进行训练。实验表明，与行业标准GPU纹理压缩格式ASTC相比，GTC实现了更高的重建质量和更低的内存使用，同时支持适用于实时渲染的随机访问、非神经解码。
英文摘要
Spatially-varying BRDFs (SVBRDFs) are central to material representation in computer graphics, but their high-resolution, multi-channel, mipmapped textures impose a substantial storage burden. Existing compression methods face a fundamental trade-off: block-based compression provides random access and hardware-friendly decoding but exploits redundancy only within local blocks; image codecs offer strong rate-distortion performance but are not designed for direct real-time texture access; and neural texture compression achieves high compression ratios but requires neural inference during decoding, which introduces additional runtime overhead, especially on mobile platforms. We present Gaussian Texture Compression (GTC), a compact 2D Gaussian-based representation for mipmapped SVBRDF texture stacks that delivers high-quality compression with flexible rate-distortion trade-offs. Our method is based on a key observation that there are two dominant sources of redundancy in such data: across mip levels and across material maps. Both share a common underlying structure: the same spatial support is reused, with only level- or map-specific information attached. This property naturally suits 2D Gaussians, since each Gaussian explicitly separates its spatial footprint from the values it carries, allowing the footprint to be shared while the values vary per level and per map. Building on this property, GTC shares Gaussians along both redundancy dimensions and is trained via a progressive optimization pipeline. Experiments show that GTC achieves higher reconstruction quality and lower memory usage than ASTC, the industry-standard GPU texture compression format, while supporting random-access, non-neural decoding suitable for real-time rendering.
