首页|A lightweight depth completion network with spatial efficient fusion

A lightweight depth completion network with spatial efficient fusion

扫码查看
原文链接
  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier B。V。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。

Depth completionLiDAR data processingLightweight networkMulti-modal fusionSpatial efficient

Fu Z.、Wu A.、Zhuang Z.、Wu X.、He J.

展开 >

School of Computer Science and Technology East China Normal University

School of Computer Science and Technology East China Normal University||Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University||Fudan University

School of Computer Science and Technology East China Normal UniversitySchool of Computer Science and Technology East China Normal University||Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University||

2025

10.1016/j.imavis.2024.105335

Image and vision computing

Image and vision computing

SCI
ISSN：0262-8856
年,卷(期)：2025.153(Jan.)
  • 77