Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reflections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artificial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by fluctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patterns, etc., those noises and patterns should be removed from captured images to recover original texture. To solve these problems, we propose three approaches; a depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method and CNN-based texture recovery method. A depth-dependent calibration is our analysis to find the acceptable depth range for approximation by center projection to find the certain target depth for calibration. In terms of CNN stereo, unlike common CNNbased stereo methods which do not consider strong disturbances like refraction or bubbles, we designed a novel CNN architecture for stereo matching using multi-scale information, which is intended to be robust against such disturbances. Finally, we propose a multi-scale method for bubble and a projected-pattern removal method using CNNs to recover original textures. Experimental results are shown to prove the effectiveness of our method compared with the state of the art techniques. Furthermore, reconstruction of a live swimming fish is demonstrated to confirm the feasibility of our techniques.
Collection of huge data for learning is another open problem for CNN-based stereo techniques. For solution, Zhou al proposed a technique without using ground truth depth data, but LR consistency as a loss function @cite_14 . Tonioni al proposed a unsupervised method by using existing stereo technique as an instruction @cite_1 . Tulyakov and Ivanov proposed a multi-instance learning (MIL) method by using several constraints and cost functions @cite_38 . We also take a similar approach to @cite_38 and use several cost functions.
@cite_38 @cite_14 @cite_1
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Deep-learning metrics have recently demonstrated extremely good performance to match image patches for stereo reconstruction. However, training such metrics requires large amount of labeled stereo images, which can be difficult or costly to collect for certain applications (consider, for example, satellite stereo imaging). The main contribution of our work is a new weakly supervised method for learning deep metrics from unlabeled stereo images, given coarse information about the scenes and the optical system. Our method alternatively optimizes the metric with a standard stochastic gradient descent, and applies stereo constraints to regularize its prediction. Experiments on reference data-sets show that, for a given network architecture, training with this new method without ground-truth produces a metric with performance as good as state-of-the-art baselines trained with the said ground-truth. This work has three practical implications. Firstly, it helps to overcome limitations of training sets, in particular noisy ground truth. Secondly it allows to use much more training data during learning. Thirdly, it allows to tune deep metric for a particular stereo system, even if ground truth is not available. Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo matching costs without human supervision. Our method updates network parameters in an iterative manner. It starts with a randomly initialized network. Left-right check is adopted to guide the training. Suitable matching is then picked and used as training data in following iterations. Our system finally converges to a stable state and performs even comparably with other supervised methods. Recent ground-breaking works have shown that deep neural networks can be trained end-to-end to regress dense disparity maps directly from image pairs. Computer generated imagery is deployed to gather the large data corpus required to train such networks, an additional fine-tuning allowing to adapt the model to work well also on real and possibly diverse environments. Yet, besides a few public datasets such as Kitti, the ground-truth needed to adapt the network to a new scenario is hardly available in practice. In this paper we propose a novel unsupervised adaptation approach that enables to fine-tune a deep learning stereo model without any ground-truth information. We rely on off-the-shelf stereo algorithms together with state-of-the-art confidence measures, the latter able to ascertain upon correctness of the measurements yielded by former. Thus, we train the network based on a novel loss-function that penalizes predictions disagreeing with the highly confident disparities provided by the algorithm and enforces a smoothness constraint. Experiments on popular datasets (KITTI 2012, KITTI 2015 and Middlebury 2014) and other challenging test images demonstrate the effectiveness of our proposal.