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Titel:

CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Tateno, K.; Tombari, F.; Laina, I.; Navab, N.
Abstract:
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM. Our fusion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along l...     »
Stichworte:
CAMP,CAMPComputerVision,ComputerVision,CVPR
Kongress- / Buchtitel:
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
Jahr:
2017
 BibTeX