On the relevance of patch-based extraction methods for monocular depth estimation

Scene geometry estimation from images plays a key role in robotics, augmented reality, and autonomous systems. In particular, Monocular Depth Estimation (MDE) focuses on predicting depth using a single RGB image, avoiding the need for expensive sensors. State-of-the-art approaches use deep learning models for MDE while processing images as a whole, sub-optimally exploiting their spatial information. A recent research direction focuses on smaller image patches, as depth information varies across different regions of an image. This approach reduces model complexity and improves performance by capturing finer spatial details. From this perspective, we propose a novel warp patch-based extraction method which corrects perspective camera distortions, and employ it in tailored training and inference pipelines. Our experimental results show that our patch-based approach outperforms its full-image-trained counterpart and the classical crop patch-based extraction. With our technique, we obtain a general performance enhancements over recent state-of-the-art models. Code is available at https://github.com/AntonioFusillo/PatchMDE . • We propose a novel patch-based approach for monocular depth estimation. • Our method extracts patches from wide-aspect images, preserving camera parameters. • The designed patch-based inference outperforms full-image models in depth accuracy. • The proposed warp-based patch extraction is superior to patch cropping. • Our approach can wrap existing models, improving their performance.