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Single-image depth estimation represents a longstanding challenge in computer vision and although it is an ill-posed problem, deep learning enabled astonishing results leveraging both supervised and self-supervised training paradigms. State-of-the-art solutions achieve remarkably accurate depth estimation from a single image deploying huge deep architectures, requiring powerful dedicated hardware to run in a reasonable amount of time. This overly demanding complexity makes them unsuited for a broad category of applications requiring devices with constrained resources or memory consumption. To tackle this issue, in this paper a family of compact, yet effective CNNs for monocular depth estimation is proposed, by leveraging self-supervision from a binocular stereo rig. Our lightweight architectures, namely PyD-Net and PyD-Net2, compared to complex state-of-the-art trade a small drop in accuracy to drastically reduce the runtime and memory requirements by a factor ranging from $2\times $ to $100\times $. Moreover, our networks can run real-time monocular depth estimation on a broad set of embedded or consumer devices, even not equipped with a GPU, by early stopping the inference with negligible (or no) loss in accuracy, making it ideally suited for real applications with strict constraints on hardware resources or power consumption. [ABSTRACT FROM AUTHOR]

Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applications, the capacity of adapting to any unseen setting becomes of paramount importance. Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. We design a lightweight and modular architecture, Modularly ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD, MAD++) which permit efficient optimization of independent sub-portions of the entire network. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. With both sources, no other data than the input images being gathered at deployment time are needed. Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression. [ABSTRACT FROM AUTHOR]

Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future. [ABSTRACT FROM AUTHOR]

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies. [ABSTRACT FROM AUTHOR]

Depth estimation is crucial in several computer vision applications, and a recent trend in this field aims at inferring such a cue from a single camera. Unfortunately, despite the compelling results achieved, state-of-the-art monocular depth estimation methods are computationally demanding, thus precluding their practical deployment in several application contexts characterized by low-power constraints. Therefore, in this paper, we propose a lightweight Convolutional Neural Network based on a shallow pyramidal architecture, referred to as $\mu $ PyD-Net, enabling monocular depth estimation on microcontrollers. The network is trained in a peculiar self-supervised manner leveraging proxy labels obtained through a traditional stereo algorithm. Moreover, we propose optimization strategies aimed at performing computations with quantized 8-bit data and map the high-level description of the network to low-level layers optimized for the target microcontroller architecture. Exhaustive experimental results on standard datasets and an in-depth evaluation with a device belonging to the popular Arm Cortex-M family confirm that obtaining sufficiently accurate monocular depth estimation on microcontrollers is feasible. To the best of our knowledge, our proposal is the first one enabling such remarkable achievement, paving the way for the deployment of monocular depth cues onto the tiny end-nodes of distributed sensor networks. [ABSTRACT FROM AUTHOR]

As reported in the stereo literature, confidence estimation represents a powerful cue to detect outliers as well as to improve depth accuracy. Purposely, we proposed a strategy enabling us to achieve state-of-the-art results by learning a confidence measure in the disparity domain only with a CNN. Since this method does not require the cost volume, it is very appealing because potentially suited for any depth-sensing technologies, including, for instance, those based on deep networks. By following this intuition, in this paper, we deeply investigate the performance of confidence estimation methods, known in the literature and new ones proposed in this paper, neglecting the use of the cost volume. Specifically, we estimate from scratch confidence measures feeding deep networks with raw depth estimates and optionally images and assess their performance deploying three datasets and three stereo algorithms. We also investigate, for the first time, their performance with disparity maps inferred by deep stereo end-to-end architectures. Moreover, we move beyond the stereo matching context, estimating confidence from depth maps generated by a monocular network. Our extensive experiments with different architectures highlight that inferring confidence prediction from the raw reference disparity only, as proposed in our previous work, is not only the most versatile solution but also the most effective one in most cases. [ABSTRACT FROM AUTHOR]

Time-of-Flight (ToF) sensors and stereo vision systems are two widely used technologies for depth estimation. Due to their rather complementary strengths and limitations, the two sensors are often combined to infer more accurate depth maps. A key research issue in this field is how to estimate the reliability of the sensed depth data. While this problem has been widely studied for stereo systems, it has been seldom considered for ToF sensors. Therefore, starting from the work done for stereo data, in this paper, we firstly introduce novel confidence estimation techniques for ToF data. Moreover, we also show how by using learning-based confidence metrics jointly trained on the two sensors yields better performance. Finally, deploying different fusion frameworks, we show how confidence estimation can be exploited in order to guide the fusion of depth data from the two sensors. Experimental results show how accurate confidence cues allow outperforming state-of-the-art data fusion schemes even with the simplest fusion strategies known in the literature. [ABSTRACT FROM AUTHOR]