IEBins: Iterative Elastic Bins for Monocular Depth Estimation
Shuwei Shao Zhongcai Pei Xingming Wu Zhong Liu Weihai Chen Zhengguo Li
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.