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Document type:
Konferenzbeitrag 
Author(s):
Lopez-Rodriguez, A.; Busam, B.; Mikolajczyk, K. 
Title:
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data 
Abstract:
Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain o...    »
 
Keywords:
ACCV,lidar,DepthCompletion,rgbd,DomainAdaptation,projection,SyntheticData 
Book / Congress title:
Asian Conference on Computer Vision 
Year:
2020