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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Bibliografiske detaljer

Principais autores:	Danish Nazir, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2022-01-01
Serier:	IEEE Access
Fag:	State-of-the-art depth completion approach on KITTI depth completion benchmark attention-based fusion for depth completion semantic-guided depth completion
Online adgang:	https://ieeexplore.ieee.org/document/9918022/
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