An automatic clusterbased approach for depth estimation of single 2D images In this paper, the problem of single 2D image depth estimation is considered. This is a very important problem due to its various applications in the industry. Previous learningbased methods are based on a key assumption that color images having photometric resemblance are likely to present similar depth structure. However, these methods search the whole dataset for finding corresponding images using handcrafted features, which is quite cumbersome and inefficient process. To overcome this, we have proposed a clusteringbased algorithm for depth estimation of a single 2D image using transfer learning. To realize this, images are categorized into clusters using Kmeans clustering algorithm and features are extracted through a pretrained deep learning model i.e., ResNet50. After clustering, an efficient step of replacing feature vector is embedded to speedup the process without compromising on accuracy. After then, images with similar structure as an input image, are retrieved from the best matched cluster based on their correlation values. Then, retrieved candidate depth images are employed to initialize prior depth of a query image using weightedcorrelationaverage (WCA). Finally, the estimated depth is improved by removing variations using crossbilateralfilter. In order to evaluate the performance of proposed algorithm, experiments are conducted on two benchmark datasets, NYU v2 and Make3D.

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.