A. Lopez-Rodriguez, B. Busam, K. Mikolajczyk Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data Asian Conference on Computer Vision (ACCV), Kyoto, Japan, November 2020 [oral]. (bib)
	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 or additional sensors. Our approach simulates the real sensor noise in an RGB + LiDAR? setup, and consists of three modules: simulating the real LiDAR? input in the synthetic domain via projections, filtering the real noisy LiDAR? for supervision and adapting the synthetic RGB image using a CycleGAN? approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.
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