Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts.

翻译：现有深度估计方法本质上局限于在离散图像网格上预测深度。此类表示限制了其向任意输出分辨率的可扩展性，并阻碍了几何细节的恢复。本文提出InfiniDepth，该方法将深度表示为神经隐式场。通过一个简单而有效的局部隐式解码器，我们可以在连续的二维坐标处查询深度，从而实现任意分辨率与细粒度的深度估计。为更好地评估本方法的性能，我们从五款不同游戏中构建了一个高质量的4K合成基准数据集，涵盖具有丰富几何与外观细节的多样化场景。大量实验表明，InfiniDepth在相对深度估计与度量深度估计任务上，于合成及真实世界基准测试中均达到了最先进的性能，尤其在精细细节区域表现优异。该方法亦有利于大视角变化下的新视角合成任务，能够生成空洞与伪影更少的高质量结果。