Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field i.e. the disparity map between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code and trained model are available on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Exploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora

Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201

Depth mapping of integral images through viewpoint image extraction with a hybrid disparity analysis algorithm

Integral imaging is a technique capable of displaying 3–D images with continuous parallax in full natural color. It is one of the most promising methods for producing smooth 3–D images. Extracting depth information from integral image has various applications ranging from remote inspection, robotic vision, medical imaging, virtual reality, to content-based image coding and manipulation for integral imaging based 3–D TV. This paper presents a method of generating a depth map from unidirectional integral images through viewpoint image extraction and using a hybrid disparity analysis algorithm combining multi-baseline, neighbourhood constraint and relaxation strategies. It is shown that a depth map having few areas of uncertainty can be obtained from both computer and photographically generated integral images using this approach. The acceptable depth maps can be achieved from photographic captured integral images containing complicated object scene