Abstract
Accurately learning high-frequency signals is a challenge in computer vision and graphics, as neural networks often struggle with these signals due to spectral bias or optimization difficulties. While current techniques like Fourier encodings have made great strides in improving performance, there remains scope for improvement when presented with high-frequency information. This paper introduces Queried-Convolutions (Qonvolutions), a simple yet powerful modification using the neighborhood properties of convolution. Qonvolution convolves a low-frequency signal with queries (such as coordinates) to enhance the learning of intricate high-frequency signals. We empirically demonstrate that Qonvolutions enhance performance across a variety of high-frequency learning tasks crucial to both the computer vision and graphics communities, including 1D regression, 2D super-resolution, 2D image regression, and novel view synthesis (NVS). In particular, by combining Gaussian splatting with Qonvolutions for NVS, we showcase state-of-the-art performance on real-world complex scenes, even outperforming powerful radiance field models on image quality.
Abstract (translated)
准确地学习高频信号是计算机视觉和图形学中的一个挑战，因为神经网络由于频谱偏差或优化难题而难以处理这些信号。尽管当前技术如傅里叶编码在提高性能方面取得了重大进展，但在面对高频信息时仍有改进空间。本文介绍了询问式卷积（Queried-Convolutions，简称Qonvolutions），这是一种简单但强大的修改方法，利用了卷积的邻域属性。通过将低频信号与查询（如坐标）进行卷积，Qonvolution能够增强复杂高频信号的学习能力。 我们实证表明，Qonvolutions在一系列对计算机视觉和图形学领域至关重要的高频学习任务中提高了性能，包括1D回归、2D超分辨率、2D图像回归以及新颖视图合成（NVS）。特别地，通过将高斯点喷射与Qonvolution结合用于NVS，我们在真实世界复杂场景上展示了最先进的表现，甚至在图像质量方面超过了强大的辐射场模型。
URL
https://arxiv.org/abs/2512.12898
