Dcpnet: A densely connected pyramid network for monocular depth estimation

doi:10.3390/s21206780

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	Dcpnet: A densely connected pyramid network for monocular depth estimation
	Z. Lai; R. Tian; Z. Wu; N. Ding; L. Sun and Y. Wang
	2021
发表期刊	Sensors
ISSN	14248220
卷号	21 期号:20
摘要	Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/s21206780
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65124
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. Lai,R. Tian,Z. Wu,et al. Dcpnet: A densely connected pyramid network for monocular depth estimation[J]. Sensors,2021,21(20).
APA	Z. Lai,R. Tian,Z. Wu,N. Ding,&L. Sun and Y. Wang.(2021).Dcpnet: A densely connected pyramid network for monocular depth estimation.Sensors,21(20).
MLA	Z. Lai,et al."Dcpnet: A densely connected pyramid network for monocular depth estimation".Sensors 21.20(2021).

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