Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State

Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State

Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor s...

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Bibliographic Details
Main Authors: Mena Nagiub, Thorsten Beuth, Ganesh Sistu, Heinrich Gotzig, Ciarán Eising
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
near field
Lidar
iTOF
depth correction
estimation
ambiguity
Online Access:https://www.mdpi.com/1424-8220/24/24/8020
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