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Titel:

Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Hao Xing, Yifan Cao, Maximilian Biber, Mingchuan Zhou, Darius Burschka
Abstract:
Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data. However, LiDAR can only generate sparse 3D maps which causes losing information. Obtaining high-quality ground-truth depth data per pixel is difficult to acquire. In order to overcome this limitation, we propose a novel approach combining structure information from a promising Plane and Parallax geometry pipeline with depth information into a U-Net supervised lear...     »
Stichworte:
Monocular depth estimation, Plane and Parallax Geometry
Dewey Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Zeitschriftentitel:
Pattern Recognition (PR) Elsevier
Jahr:
2022
Volltext / DOI:
doi:10.1016/j.patcog.2022.108806
WWW:
Reference video
Status:
Verlagsversion / published
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