Single Image Depth Estimation Trained via Depth from Defocus Cues. Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learning of depth from a single RGB image, depth is not given explicitly. Existing work in the field receives either a stereo pair, a monocular video, or multiple views, and, using losses that are based on structurefrommotion, trains a depth estimation network. In this work, we rely, instead of different views, on depth from focus cues. Learning is based on a novel Point Spread Function convolutional layer, which applies location specific kernels that arise from the CircleOfConfusion in each image location. We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches. Since the phenomenon of depth from defocus is not dataset specific, we hypothesize that learning based on it would overfit less to the specific content in each dataset. Our experiments show that this is indeed the case, and an estimator learned on one dataset using our method provides better results on other datasets, than the directly supervised methods.

Monocular Depth Estimation in New Environments With Absolute Scale In this work we propose an unsupervised training method that finetunes a single image depth estimation CNN towards a new environment. The network, which has been pretrained on stereo data, only requires monocular input for finetuning. Unlike other unsupervised methods, it produces depth estimations with absolute scale xe2x80x93 a feature that is essential for most practical applications, yet has mostly been overlooked in the literature. First, we show how our method allows adapting a network trained on one dataset (Cityscapes) to another (KITTI). Next, by splitting KITTI in subsets, we show the sensitivity of pretrained models to a domain shift. We then demonstrate that, by finetuning the model using our method, it is possible to improve the performance on the target subset, without using stereo or any form of groundtruth depth and with preservation of the correct absolute scale.