We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by 50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two applications of the proposed algorithm: a plug-in module in SLAM to convert sparse maps to dense maps, and super-resolution for LiDARs. Software 2 and video demonstration 3 are publicly available.
Ma and Karaman REF proposed sparse-to-dense deep network which utilizes an RGB image and a randomly sampled set of sparse depth measurements to produce a dense depth map.
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
2018 IEEE International Conference on Robotics and Automation (ICRA)
2018 IEEE International Conference on Robotics and Automation (ICRA)
Computer Science Engineering