发表机构
University of Toronto; Stanford University; University of Waterloo（多伦多大学; 斯坦福大学; 滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多分辨率哈希编码中哈希碰撞导致的容量分配不均问题，提出碰撞感知分辨率自适应方法，通过平衡各层信息负载并引入可逆像素洗牌变换，在减少参数的同时提升图像拟合的保真度。
AI 中文摘要
多分辨率哈希编码最近通过沿几何分辨率调度将多尺度特征存储在固定大小的哈希表中，实现了快速且高保真的隐式神经表示。然而，标准设计是数据无关的：不同的分辨率级别获得相同的哈希表容量，尽管图像频率内容差异很大。因此，一些级别经历严重的哈希碰撞，而其他级别则参数利用不足，导致容量分配效率低下。为了解决这个问题，我们提出了碰撞感知分辨率自适应（CARA），一种通过平衡各哈希级别的有效信息负载来分配每级分辨率的方法。这种自适应分配减少了容量瓶颈并提高了参数效率。此外，我们引入了一种可逆的像素洗牌变换，通过重新分配空间信息来降低哈希负载因子，从而在不扩大哈希表的情况下减轻碰撞引起的信息损失。为了支持对极高分辨率数据的评估，我们还整理了据我们所知第一个用于学术研究的未压缩全幻灯片图像数据集。在Kodak图像、十亿像素自然图像和原始全幻灯片图像上的实验表明，CARA持续改善了保真度-参数权衡。我们的方法在使用仅27.76%的参数时达到最先进的性能，并在相当的参数数量下实现了高达6.11 dB的PSNR改进。代码在补充材料中提供。
英文摘要
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.
Comments32 pages, 12 figures, ECCV 2026
