用小波构建通用视觉基元，实现快速高质渲染。
WIPES: Wavelet-based Visual Primitives
- 基于小波的多维视觉信号表示，兼具频域灵活性与空间定位优势。
- 在2D图像和5/6维新视角生成任务中，渲染质量优于INR方法，速度更快。
- 支持静态与动态场景，适合需要高效高质量渲染的视觉建模任务。
追求连续视觉表示以实现灵活的频率调节和快速渲染，近年来在三维视觉与图形学领域备受关注。然而，现有方法通常依赖频率引导或复杂的神经网络解码，导致频谱损失或渲染缓慢。为此，我们提出WIPES——一种通用的小波基视觉基元，用于表示多维视觉信号。依托小波在时空-频率上的局部化优势，WIPES能有效捕捉低频‘森林’与高频‘树木’。同时，我们设计了一种小波基可微光栅化器，实现快速可视化渲染。在多种视觉任务上的实验结果表明，包括2D图像表示、5维静态及6维动态新视角合成，作为视觉基元的WIPES在渲染质量上优于基于INR的方法，且推理速度更快；在渲染质量上也超过基于高斯的表示。
原文摘要 · Abstract (English)
Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision and graphics. However, existing representations often rely on frequency guidance or complex neural network decoding, leading to spectrum loss or slow rendering. To address these limitations, we propose WIPES, a universal Wavelet-based vIsual PrimitivES for representing multi-dimensional visual signals. Building on the spatial-frequency localization advantages of wavelets, WIPES effectively captures both the low-frequency "forest" and the high-frequency "trees." Additionally, we develop a wavelet-based differentiable rasterizer to achieve fast visual rendering. Experimental results on various visual tasks, including 2D image representation, 5D static and 6D dynamic novel view synthesis, demonstrate that WIPES, as a visual primitive, offers higher rendering quality and faster inference than INR-based methods, and outperforms Gaussian-based representations in rendering quality.
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