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

• URL: http://arxiv.org/abs/2506.05655v1
• Date: Fri, 06 Jun 2025 01:14:55 GMT
• Title: Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues
• Authors: Yimei Liu, Yakun Ju, Yuan Rao, Hao Fan, Junyu Dong, Feng Gao, Qian Du,
• Abstract summary: We propose an Adaptive Depth Range MVS ( ADR-MVS) to improve multi-view depth estimation accuracy.<n> ADR-MVS generates adaptive range maps from depth and normal estimates using cross-attention discrepancy learning.<n> Experimental results demonstrate that ADR-MVS achieves state-of-the-art performance on the WHU, LuoJia-MVS, and M"unchen datasets.
• Score: 38.954104931025704
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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