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In this paper, we propose a new method for passive depth estimation based on the combination of a camera with longitudinal chromatic aberration and an original depth from defocus (DFD) algorithm. Indeed a chromatic lens, combined with an RGB sensor, produces three images with spectrally variable in-focus planes, which eases the task of depth extraction with DFD. We first propose an original DFD algorithm dedicated to color images having spectrally varying defocus blurs. Then we describe the design of a prototype chromatic camera so as to evaluate experimentally the effectiveness of the proposed approach for depth estimation. We provide comparisons with results of an active ranging sensor and real indoor/outdoor scene reconstructions.

Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to predict corresponding depth maps. However, cameras can also produce images with defocus blur depending on the depth of the objects and camera settings. Hence, these features may represent an important hint for learning to predict depth. In this paper, we propose a full system for single-image depth prediction in the wild using depth-fromdefocus and neural networks. We carry out thorough experiments to test deep convolutional networks on real and simulated defocused images using a realistic model of blur variation with respect to depth. We also investigate the influence of blur on depth prediction observing model uncertainty with a Bayesian neural network approach. From these studies, we show that out-of-focus blur greatly improves the depth-prediction network performances. Furthermore, we transfer the ability learned on a synthetic, indoor dataset to real, indoor and outdoor images. For this purpose, we present a new dataset containing real all-focus and defocused images from a Digital Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained with an active 3D sensor for indoor scenes. The proposed approach is successfully validated on both this new dataset and standard ones as NYUv2 or Depth-in-the-Wild. Code and new datasets are available at https:// github.com/ marcelampc/ d3net depth estimation.

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