Monocular depth estimation (MDE) in aerial scenarios presents a significant challenge due to various factors such as varying altitudes, perspective distortions, and occlusions. Leveraging scene priors, like surfaces, can preserve structural integrity, with semantic segmentation serving as a valuable tool for attaining this objective. This work introduces a novel approach to depth estimation in aerial scenarios by integrating semantic information with depth features. Thus, we propose a novel learnable module, called semBins, that combines semantics with depth features at the depth discretization level in the MDE network. To maximize the effectiveness of this technique, a novel pseudo-random semantic-based query-response training procedure is proposed, which extracts image patches and predicts depth distributions of local neighborhoods for each pixel. This approach ensures that the semantic labels guide the patch extraction process, narrowing down the bin distribution search space. We show that semBins can achieve top performance on both synthetic and real-life aerial-based datasets.


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    Title :

    SemBins: Semantic Bins for Monocular Depth Estimation in Aerial Scenarios


    Contributors:


    Publication date :

    2024-09-24


    Size :

    10460928 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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