Shao, S., Pei, Z., Chen, W. et al. IEBins: Iterative Elastic Bins for Monocular Depth Estimation and Completion. Int J Comput Vis 133, 2463–2486 (2025). https://doi.org/10.1007/s11263-024-02293-3
Abstract:
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
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Robotics Horizontal Technology Coordinating Office
Grant Reference no. : C221518005
Description:
This is a post-peer-review, pre-copyedit version of an article published in International Journal of Computer Vision. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11263-024-02293-3