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CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image

Cited 2 time in Web of Science Cited 3 time in Scopus

Kam, Jaewon; Kim, Jungeon; Kim, Soongjin; Park, Jaesik; Lee, Seungyong

Issue Date
Springer Verlag
Lecture Notes in Computer Science, Vol.13662, pp.257-274
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.
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning


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