InfiniDepth：基于神经隐式场的任意分辨率与细粒度深度估计

摘要

现有深度估计方法本质上受限于在离散图像网格上预测深度。此类表示形式限制了其向任意输出分辨率的扩展能力，并阻碍了几何细节的恢复。本文提出InfiniDepth方法，将深度表示为神经隐式场。通过一种简单而有效的局部隐式解码器，我们可以在连续二维坐标上查询深度，实现任意分辨率与细粒度的深度估计。为更好评估本方法性能，我们从五款不同游戏中精心构建了高质量的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在相对深度估计和度量深度估计任务中，于合成与真实场景基准测试上均达到最先进性能，尤其在精细细节区域表现卓越。该方法还能显著提升大视角变化下的新视角合成任务效果，生成孔洞更少、伪影更少的高质量结果。

English

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.

InfiniDepth：基于神经隐式场的任意分辨率与细粒度深度估计

InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

摘要

Support