Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues

TL;DR

This paper introduces ADR-MVS, an adaptive multi-view stereo method for aerial images that leverages monocular cues and normal guidance to improve depth estimation accuracy and robustness in urban reconstruction tasks.

Contribution

The paper proposes a novel adaptive depth range inference framework integrating monocular cues and normal guidance, tailored for aerial multi-view stereo to enhance accuracy and efficiency.

Findings

Achieves state-of-the-art performance on multiple aerial datasets.

Improves feature matching by adaptive depth range prediction.

Outperforms existing RGB-guided depth refinement methods.

Abstract

Three-dimensional digital urban reconstruction from multi-view aerial images is a critical application where deep multi-view stereo (MVS) methods outperform traditional techniques. However, existing methods commonly overlook the key differences between aerial and close-range settings, such as varying depth ranges along epipolar lines and insensitive feature-matching associated with low-detailed aerial images. To address these issues, we propose an Adaptive Depth Range MVS (ADR-MVS), which integrates monocular geometric cues to improve multi-view depth estimation accuracy. The key component of ADR-MVS is the depth range predictor, which generates adaptive range maps from depth and normal estimates using cross-attention discrepancy learning. In the first stage, the range map derived from monocular cues breaks through predefined depth boundaries, improving feature-matching discriminability and mitigating convergence to local optima. In later stages, the inferred range maps are progressively narrowed, ultimately aligning with the cascaded MVS framework for precise depth regression. Moreover, a normal-guided cost aggregation operation is specially devised for aerial stereo images to improve geometric awareness within the cost volume. Finally, we introduce a normal-guided depth refinement module that surpasses existing RGB-guided techniques. Experimental results demonstrate that ADR-MVS achieves state-of-the-art performance on the WHU, LuoJia-MVS, and M\"unchen datasets, while exhibits superior computational complexity.