IEBins: Iterative Elastic Bins for Monocular Depth Estimation and Completion

Monocular depth estimation and completion are fundamental aspects of geometric computer vision, serving as essential techniques for various downstream applications. In recent developments, several methods have reformulated these two tasks as a classification-regression problem, deriving depth with a linear combination of predicted probabilistic distribution and bin centers. In this paper, we introduce an innovative concept termed iterative elastic bins (IEBins) for the classification-regression-based monocular depth estimation and completion. The IEBins involves the idea of iterative division of bins. In the initialization stage, a coarse and uniform discretization is applied to the entire depth range. Subsequent update stages then iteratively identify and uniformly discretize the target bin, by leveraging it as the new depth range for further refinement. To mitigate the risk of error accumulation during iterations, we propose a novel elastic target bin, replacing the original one. The width of this elastic bin is dynamically adapted according to the depth uncertainty. Furthermore, we develop dedicated frameworks to instantiate the IEBins. Extensive experiments on the KITTI, NYU-Depth-v2, SUN RGB-D, ScanNet and DIODE datasets indicate that our method outperforms prior state-of-the-art monocular depth estimation and completion competitors.