Unsupervised monocular depth estimation based on edge enhancement

被引：0
|
作者：
Qu Y. [1 ]
Chen Y. [1 ]
机构：
[1] Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 01期
关键词：
edge enhancement; monocular depth estimation; strip convolutions; unsupervised learning;
D O I：
10.12305/j.issn.1001-506X.2024.01.08
中图分类号：
学科分类号：
摘要：
To solve the problem of poor edge depth estimation accuracy in unsupervised monocular depth estimation, an unsupervised monocular depth estimation model based on edge enhancement is proposed. The model is composed of a single-view depth network and a camera pose estimation network, both of which adopt encoder-decoder structures. The single-view depth network encoder uses high-resolution net (HRNet) as the backbone which maintains high resolution representations throughout the whole process, and is conducive to extract accurate spatial features; The single-view depth network decoder introduces strip convolutions to refine the depth variations near the edges, while enhancing the edge details using the classical Laplace of Gaussian operator. The method fully utilizes the depth edge information to improve the quality of the depth estimation. The experimental results on the KITTI dataset show that the proposed model has good depth estimation performance, making the edges of the depth map clearer with more details. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：71 / 79
页数：8
相关论文
共 34 条
  • [1] KIRAN B R, SOBH I, TALPAERT V, Et al., Deep reinforcement learning for autonomous driving: a survey, IEEE Trans.on Intelligent Transportation Systems, 23, 6, pp. 4909-4926, (2022)
  • [2] ZENG A, SONG S, NIEBNER M, Et al., 3D Match: learning local geometric descrip-tors from RGB-D reconstructions, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 199-208, (2017)
  • [3] MACARIO B A, MICHEL M, MOLINE Y, Et al., A comprehensive survey of visual slam algorithms, Robotics, (2022)
  • [4] CHEN K Q, ZHU Z L, DENG X M, Et al., A survey of deep learning research on multi-scale target detection[J], Journal of Software, 32, 4, pp. 1201-1227, (2021)
  • [5] CAO Z Q, SAI B, LYU X, A survey of pedestrian tracking algorithms and applications[J], Acta Physica Sinica, 69, 8, pp. 41-58, (2020)
  • [6] HOU L, LUO X Y, WANG Z Y, Et al., Representation learning via a semi-supervised stacked distance autoencoder for image classification, Frontiers of Information Technology & Electronic Engineering, 21, 7, pp. 1005-1019, (2020)
  • [7] EIGEN D, PUHRSCH C, FERGUS R, Depth map prediction from a single image using a multi-scale deep network, Advances in neural information processing systems, 27, pp. 2366-2374, (2014)
  • [8] LAINA I, RUPPRECHT C, BELAGIANNIS V, Et al., Deeper depth prediction with fully convolutional residual networks, Proc. of the 4th IEEE Conference on International Conference on 3D Vision, pp. 239-248, (2016)
  • [9] FU H, GONG M M, WANG C H, Et al., Deep ordinal regression network for monocular depth estimation, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2002-2011, (2018)
  • [10] YUAN W H, GU X D, DAI Z Z, Et al., Neural window fully-connected CRFs for monocular depth estimation, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3906-3915, (2022)
1234