Publications
Detailed Information
CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image
Cited 2 time in
Web of Science
Cited 3 time in Scopus
- Authors
- Issue Date
- 2022
- Publisher
- Springer Verlag
- Citation
- Lecture Notes in Computer Science, Vol.13662, pp.257-274
- Abstract
- Successful depth completion from a single RGB-D image requires both extracting plentiful 2D and 3D features and merging these heterogeneous features appropriately. We propose a novel depth completion framework, CostDCNet, based on the cost volume-based depth estimation approach that has been successfully employed for multi-view stereo (MVS). The key to high-quality depth map estimation in the approach is constructing an accurate cost volume. To produce a quality cost volume tailored to single-view depth completion, we present a simple but effective architecture that can fully exploit the 3D information, three options to make an RGB-D feature volume, and per-plane pixel shuffle for efficient volume upsampling. Our CostDCNet framework consists of lightweight deep neural networks (∼ 1.8M parameters), running in real time (∼ 30 ms). Nevertheless, thanks to our simple but effective design, CostDCNet demonstrates depth completion results comparable to or better than the state-of-the-art methods.
- ISSN
- 0302-9743
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.