Geometric Edge Modelling in Self-Supervised Learning for Enhanced Indoor Depth Estimation

Abstract

Recently, the accuracy of self-supervised deep learning models for indoor depth estimation has approached that of supervised models by improving the supervision in planar regions. However, a common issue with integrating multiple planar priors is the generation of oversmooth depth maps, leading to unrealistic and erroneous depth representations at edges. Despite the fact that edge pixels only cover a small part of the image, they are of high significance for downstream tasks such as visual odometry, where image features, essential for motion computation, are mostly located at edges. To improve erroneous depth predictions at edge regions, we delve into the self-supervised training process, identifying its limitations and using these insights to develop a geometric edge model. Building on this, we introduce a novel algorithm that utilises the smooth depth predictions of existing models and colour image data to accurately identify edge pixels. After finding the edge pixels, our approach generates targeted self-supervision in these zones by interpolating depth values from adjacent planar areas towards the edges. We integrate the proposed algorithms into a novel loss function that encourages neural networks to predict sharper and more accurate depth edges in indoor scenes. To validate our methodology, we incorporated the proposed edge-enhancing loss function into a state-of-the-art self-supervised depth estimation framework. Our results demonstrate a notable improvement in the accuracy of edge depth predictions and a 19% improvement in visual odometry when using our depth model to generate RGB-D input, compared to the baseline model.