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

2026-01-06

arXiv.org

AI Summary

This work addresses the limitations of conventional depth estimation methods, which are constrained by discrete image grids and thus struggle to recover fine geometric details or support arbitrary-resolution outputs. To overcome this, we propose a continuous depth representation based on neural implicit fields, introducing a local implicit decoder that enables high-fidelity depth querying at any 2D coordinate. To facilitate training and evaluation, we construct a high-resolution 4K synthetic dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on both synthetic and real-world datasets, significantly enhancing geometric detail recovery and substantially improving the quality of novel-view synthesis under large viewpoint changes.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• depth estimation
• arbitrary resolution
• fine-grained geometry
• neural implicit fields
• continuous representation

Innovation

• neural implicit fields
• arbitrary-resolution depth estimation
• fine-grained depth
• local implicit decoder
• novel view synthesis