IEBins:用于单目深度估算和完成的迭代弹性分区

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shuwei Shao, Zhongcai Pei, Weihai Chen, Peter C. Y. Chen, Zhengguo Li
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引用次数: 0

摘要

单目深度估计和完成是几何计算机视觉的基本方面,是各种下游应用的基本技术。在最近的发展中,有几种方法将这两项任务重新表述为一个分类-回归问题,通过预测概率分布和箱中心的线性组合得出深度。在本文中,我们为基于分类-回归的单目深度估计和完成引入了一个创新概念,称为迭代弹性仓(IEBins)。IEBins 包含迭代分仓的思想。在初始化阶段,对整个深度范围进行粗略而均匀的离散化。随后的更新阶段会反复识别并统一离散化目标仓,将其作为进一步细化的新深度范围。为了降低迭代过程中误差累积的风险,我们提出了一种新的弹性目标仓,以取代原来的目标仓。这种弹性目标仓的宽度可根据深度的不确定性进行动态调整。此外,我们还开发了专用框架来实例化 IEBins。在 KITTI、NYU-Depth-v2、SUN RGB-D、ScanNet 和 DIODE 数据集上进行的大量实验表明,我们的方法优于之前最先进的单目深度估计和补全竞争对手。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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