Zhichao Fu , Anran Wu , Zisong Zhuang , Xingjiao Wu , Jun He
{"title":"A lightweight depth completion network with spatial efficient fusion","authors":"Zhichao Fu , Anran Wu , Zisong Zhuang , Xingjiao Wu , Jun He","doi":"10.1016/j.imavis.2024.105335","DOIUrl":null,"url":null,"abstract":"<div><div>Depth completion is a low-level task rebuilding the dense depth from a sparse set of measurements from LiDAR sensors and corresponding RGB images. Current state-of-the-art depth completion methods used complicated network designs with much computational cost increase, which is incompatible with the realistic-scenario limited computational environment. In this paper, we explore a lightweight and efficient depth completion model named Light-SEF. Light-SEF is a two-stage framework that introduces local fusion and global fusion modules to extract and fuse local and global information in the sparse LiDAR data and RGB images. We also propose a unit convolutional structure named spatial efficient block (SEB), which has a lightweight design and extracts spatial features efficiently. As the unit block of the whole network, SEB is much more cost-efficient compared to the baseline design. Experimental results on the KITTI benchmark demonstrate that our Light-SEF achieves significant declines in computational cost (about 53% parameters, 50% FLOPs & MACs, and 36% running time) while showing competitive results compared to state-of-the-art methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"153 ","pages":"Article 105335"},"PeriodicalIF":4.2000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624004402","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Depth completion is a low-level task rebuilding the dense depth from a sparse set of measurements from LiDAR sensors and corresponding RGB images. Current state-of-the-art depth completion methods used complicated network designs with much computational cost increase, which is incompatible with the realistic-scenario limited computational environment. In this paper, we explore a lightweight and efficient depth completion model named Light-SEF. Light-SEF is a two-stage framework that introduces local fusion and global fusion modules to extract and fuse local and global information in the sparse LiDAR data and RGB images. We also propose a unit convolutional structure named spatial efficient block (SEB), which has a lightweight design and extracts spatial features efficiently. As the unit block of the whole network, SEB is much more cost-efficient compared to the baseline design. Experimental results on the KITTI benchmark demonstrate that our Light-SEF achieves significant declines in computational cost (about 53% parameters, 50% FLOPs & MACs, and 36% running time) while showing competitive results compared to state-of-the-art methods.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.