SemAttNet: Toward Attention-Based Semantic Aware Guided Depth Completion

SemAttNet: Toward Attention-Based Semantic Aware Guided Depth Completion

Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene....

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Bibliographic Details
Main Authors: Danish Nazir, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
State-of-the-art depth completion approach on KITTI depth completion benchmark
attention-based fusion for depth completion
semantic-guided depth completion
Online Access:https://ieeexplore.ieee.org/document/9918022/

Internet

https://ieeexplore.ieee.org/document/9918022/

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