Monocular depth estimation (MDE) is a fundamental topic of geometric computer
vision and a core technique for many downstream applications. Recently, several
methods reframe the MDE as a classification-regression problem where a linear
combination of probabilistic distribution and bin centers is used to predict depth.
In this paper, we propose a novel concept of iterative elastic bins (IEBins) for
the classification-regression-based MDE. The proposed IEBins aims to search for
high-quality depth by progressively optimizing the search range, which involves
multiple stages and each stage performs a finer-grained depth search in the target
bin on top of its previous stage. To alleviate the possible error accumulation during
the iterative process, we utilize a novel elastic target bin to replace the original
target bin, the width of which is adjusted elastically based on the depth uncertainty.
Furthermore, we develop a dedicated framework composed of a feature extractor
and an iterative optimizer that has powerful temporal context modeling capabilities
benefiting from the GRU-based architecture. Extensive experiments on the KITTI,
NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method
surpasses prior state-of-the-art competitors. The source code is publicly available
at https://github.com/ShuweiShao/IEBins.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Robotics Horizontal Technology Coordinating Office (HTCO)
Grant Reference no. : C221518005
This work was supported in part by the National Natural Science Foundation of China under grant U1909215 and 51975029, in part by the Key Research and Development Program of Zhejiang Province under Grant 2021C03050, in part by the Scientific Research Project of Agriculture and Social Development of Hangzhou under Grant No. 20212013B11, and in part by the National Natural Science Foundation of China under grant 61620106012 and 61573048.