CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fitting

2026-09-16

AI Summary

为解决多分辨率哈希编码中因数据无关设计导致的哈希碰撞和参数利用不均问题，提出CARA方法，通过自适应分配各层分辨率并引入可逆像素洗牌变换来提高参数效率和图像拟合质量。

Abstract

Multiresolution hash encodings have recently enabled fast and high-fidelity implicit neural representations by storing multi-scale features in fixed-size hash tables along a geometric resolution schedule. However, the standard design is data-agnostic: different resolution levels receive identical hash-table capacity despite large differences in image frequency content. As a result, some levels experience severe hash collisions while others underutilize parameters, leading to inefficient capacity allocation. To address this issue, we propose Collision-Aware Resolution Adaptation (CARA), a method that assigns per-level resolutions by balancing the effective information load across hash levels. This adaptive allocation reduces capacity bottlenecks and improves parameter efficiency. In addition, we introduce an invertible pixel-shuffle transform that reduces hash load factors by redistributing spatial information, thereby mitigating collision-induced information loss without enlarging the hash tables. To support evaluation on extremely high-resolution data, we also curate, to the best of our knowledge, the first uncompressed whole-slide image dataset for academic research. Experiments on Kodak images, gigapixel natural images, and raw whole-slide images demonstrate that CARA consistently improves the fidelity-parameter trade-off. Our method matches state-of-the-art performance while using only $27.76%$ of the parameters, and achieves up to $6.11$ dB PSNR improvement at comparable parameter counts. Code is provided in the supplementary.

Problem

• multiresolution hash encodings
• hash collisions
• capacity allocation

Innovation

• Collision-Aware Resolution Adaptation
• adaptive allocation
• invertible pixel-shuffle transform
• parameter efficiency