Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image synthesis on sequences or stereo pairs. Focusing on this second case, in this paper we leverage stereo matching in order to improve monocular depth estimation. To this aim we propose monoResMatch, a novel deep architecture designed to infer depth from a single input image by synthesizing features from a different point of view, horizontally aligned with the input image, performing stereo matching between the two cues. In contrast to previous works sharing this rationale, our network is the first trained end-to-end from scratch. Moreover, we show how obtaining proxy ground truth annotation through traditional stereo algorithms, such as Semi-Global Matching, enables more accurate monocular depth estimation still countering the need for expensive depth labels by keeping a self-supervised approach. Exhaustive experimental results prove how the synergy between i) the proposed monoResMatch architecture and ii) proxy-supervision attains state-of-the-art for self-supervised monocular depth estimation. The code is publicly available at this https URL.
figures network.tex Since for most tasks ground truth labels are difficult and expensive to source, some works recently enquired about the possibility to replace them with easier to obtain proxy labels. Tonioni al @cite_43 proposed to adapt deep stereo networks to unseen environments leveraging traditional stereo algorithms and confidence measures @cite_31 , Tosi al @cite_59 learned confidence estimation selecting positive and negative matches by means of traditional confidence measures, Makansi al @cite_44 and Liu al @cite_8 generated proxy labels for training optical flow networks using conventional methods. Specifically relevant to monocular depth estimation are the works proposed by Yang al @cite_35 , using stereo visual odometry to train monocular depth estimation, by Klodt and Vedaldi @cite_45 , leveraging structure from motion algorithms and by Guo al @cite_15 , obtaining labels from a deep network trained with supervision to infer disparity maps from stereo pairs.
@cite_35 @cite_8 @cite_15 @cite_44 @cite_43 @cite_45 @cite_59 @cite_31
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We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches. A novel post-processing step is then introduced, which employs a second deep convolutional neural network for pooling global information from multiple disparities. This network outputs both the image disparity map, which replaces the conventional "winner takes all" strategy, and a confidence in the prediction. The confidence score is achieved by training the network with a new technique that we call the reflective loss. Lastly, the learned confidence is employed in order to better detect outliers in the refinement step. The proposed pipeline achieves state of the art accuracy on the largest and most competitive stereo benchmarks, and the learned confidence is shown to outperform all existing alternatives. This paper presents a deep network based unsupervised visual odometry system for 6-DoF camera pose estimation and finding dense depth map for its monocular view. The proposed network is trained using unlabeled binocular stereo image pairs and is shown to provide superior performance in depth and ego-motion estimation compared to the existing state-of-the-art. This is achieved by introducing a novel objective function and training the network using temporally alligned sequences of monocular images. The objective function is based on the Charbonnier penalty applied to spatial and bi-directional temporal reconstruction losses. The overall novelty of the approach lies in the fact that the proposed deep framework combines a disparity-based depth estimation network with a pose estimation network to obtain absolute scale-aware 6-DoF camera pose and superior depth map. According to our knowledge, such a framework with complete unsupervised end-to-end learning has not been tried so far, making it a novel contribution in the field. The effectiveness of the approach is demonstrated through performance comparison with the state-of-the-art methods on KITTI driving dataset. 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. Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error. While this is a simple concept well-known in stereo matching, to make it work in a deep-learning framework, many non-trivial challenges must be overcome, and in this work we provide effective solutions. Our network is self-adaptive to different unseen imageries as well as to different camera settings. Experiments on KITTI and Middlebury stereo benchmark datasets show that our method outperforms many state-of-the-art stereo matching methods with a margin, and at the same time significantly faster.