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Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lambertian materials and effectively processing high-resolution images. Purposely, we propose a novel dataset that includes accurate and dense ground-truth labels at high resolution, featuring scenes containing several specular and transparent surfaces. Our acquisition pipeline leverages a novel deep space-time stereo framework, enabling easy and accurate labeling with sub-pixel precision. The dataset is composed of 606 samples collected in 85 different scenes, each sample includes both a high-resolution pair (12 Mpx) as well as an unbalanced stereo pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We divide the dataset into a training set, and two testing sets, the latter devoted to the evaluation of stereo and monocular depth estimation networks respectively to highlight the open challenges and future research directions in this field., Extension of the paper "Open Challenges in Deep Stereo: the Booster Dataset" that was presented at CVPR 2022

Precision Livestock Farming relies on several technological approaches to acquire in the most efficient way precise and up-to-date data concerning individual animals. In dairy farming, particular attention is paid to the automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Computer vision represents a suitable and promising method for this purpose. This paper describes the first step for the development of a computer vision system, based on deep learning, aiming to recognize in real-time the individual cows, detect their positions, actions and movements and record the time history outputs for each animal. Specifically, a neural network based on deep learning techniques has been trained and validated on a case study farm, for the automatic recognition of individual cows in videos recorded in the barn. Four cows were selected to train and validate a YOLO neural network able to recognize a cow starting from the coat pattern. Then, precision-recall curves of the identification of individual cows were elaborated for both the specific target classes and the whole dataset in order to assess the performances of the network. By means of data augmentation techniques, an enlarged dataset has been created and considered in order to improve the performance of the network and to provide indications to increase detection efficiency in those cases where data acquisition is not easy to be carried out for long periods. The mean average precision of the detection, ranging from 0.64 to 0.66, showed that it is possible to properly identify individual cows based on their morphological appearance and that the piebald spotting pattern of a cow’s coat represents a clearly distinguishable object for a computer vision network. The results also led to obtain indications about the quantity and the characteristics of the images to be used for the network training in order to achieve efficient detections when facing with applications involving animals.

Stereo vision is a popular technique to infer depth from two or more images. In this field, confidence measures, typically obtained from the analysis of the cost volume, aim at detecting uncertain disparity assignments. As recently proved, multiple confidence measures combined with hand-crafted features extracted from the cost volume can be used also for other purposes and in particular to improve the overall disparity accuracy leveraging on machine learning techniques. In this paper, starting from the observation that recurrent local patterns occurring in the disparity maps can tell a correct assignment from a wrong one, we follow a completely different methodology to infer a novel confidence measure from scratch. Specifically, leveraging on Convolutional Neural Networks, we pose the confidence formulation as a regression problem by analyzing the disparity map provided by a stereo vision system. Once trained on a subset of the KITTI 2012 dataset with the disparity maps provided by the simple block-matching algorithm, our confidence measure outperforms state-of-the-art with two datasets (KITTI 2015 and Middlebury 2014) as well as with two stereo algorithms. The experimental evaluation reported clearly highlights that our approach is capable to better generalize its behavior in different circumstances with respect to state-of-the-art. Finally, not being based on cost volume analysis, our proposal is also potentially suited for out-of-the-box depth generation devices which usually do not expose the cues required by top-performing approaches.

We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research., CVPR 2022, New Orleans. Project page: https://cvlab-unibo.github.io/booster-web/

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 μPyD-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.

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× to 100×. 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.

The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called social distance gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner., Accepted for pubblication on IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotated with semi-dense, high-resolution ground-truth labels in the form of disparity maps. To tackle the task, we propose a deep learning architecture trained in a self-supervised manner by exploiting a further RGB camera, required only during training data acquisition. In this setup, we can conveniently learn cross-modal matching in the absence of ground-truth labels by distilling knowledge from an easier RGB-RGB matching task based on a collection of about 11K unlabeled image triplets. Experiments show that the proposed pipeline sets a good performance bar (1.16 pixels average registration error) for future research on this novel, challenging task., Comment: CVPR 2022, New Orleans. Project page: https://cvlab-unibo.github.io/rgb-ms-web/

Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit its practical deployment: i) the low reliability when deployed in-the-wild and ii) the demanding resource requirements to achieve real-time performance, often not compatible with such devices. Therefore, in this paper, we deeply investigate these issues showing how they are both addressable adopting appropriate network design and training strategies -- also outlining how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time depth-aware augmented reality and image blurring with smartphones in-the-wild., Comment: 11 pages, 9 figures

Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at variable flow regimes. Nonetheless, to tackle their computational and energy requirements, offload processing and high-speed internet connections in the monitored environments, which are often difficult to access, is mandatory hence limiting the effective deployment of such techniques in several relevant circumstances. In this paper, we advance and simplify streamflow velocity monitoring by directly processing the image stream in situ with a low-power embedded system. By leveraging its standard parallel processing capability and exploiting functional simplifications, we achieve an accuracy comparable to state-of-the-art algorithms that typically require expensive computing devices and infrastructures. The advantage of monitoring streamflow velocity in situ with a lightweight and cost-effective embedded processing device is threefold. First, it circumvents the need for wideband internet connections, which are expensive and impractical in remote environments. Second, it massively reduces the overall energy consumption, bandwidth and deployment cost. Third, when monitoring more than one river section, processing “at the very edge” of the system efficiency improves scalability by a large margin, compared to offload solutions based on remote or cloud processing. Therefore, enabling streamflow velocity monitoring in situ with low-cost embedded devices would foster the widespread diffusion of gauge cameras even in developing countries where appropriate infrastructure might be not available or too expensive.

Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MC-CNN and SGM), as well as a deep stereo network (PSM-Net). We also evaluate how well confidence measures generalize to different environments/datasets.

State-of-the-art approaches to infer dense depth measurements from images rely on CNNs trained end-to-end on a vast amount of data. However, these approaches suffer a drastic drop in accuracy when dealing with environments much different in appearance and/or context from those observed at training time. This domain shift issue is usually addressed by fine-tuning on smaller sets of images from the target domain annotated with depth labels. Unfortunately, relying on such supervised labeling is seldom feasible in most practical settings. Therefore, we propose an unsupervised domain adaptation technique which does not require groundtruth labels. Our method relies only on image pairs and leverages on classical stereo algorithms to produce disparity measurements alongside with confidence estimators to assess upon their reliability. We propose to fine-tune both depth-from-stereo as well as depth-from-mono architectures by a novel confidence-guided loss function that handles the measured disparities as noisy labels weighted according to the estimated confidence. Extensive experimental results based on standard datasets and evaluation protocols prove that our technique can address effectively the domain shift issue with both stereo and monocular depth prediction architectures and outperforms other state-of-the-art unsupervised loss functions that may be alternatively deployed to pursue domain adaptation., Comment: 14 pages, 7 pages. Accepted to TPAMI

Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing. CNNs led to considerable improvements in this field, and recent trends replaced the need for ground-truth labels with geometry-guided image reconstruction signals enabling unsupervised training. Currently, for this purpose, state-of-the-art techniques rely on images acquired with a binocular stereo rig to predict inverse depth (i.e., disparity) according to the aforementioned supervision principle. However, these methods suffer from well-known problems near occlusions, left image border, etc inherited from the stereo setup. Therefore, in this paper, we tackle these issues by moving to a trinocular domain for training. Assuming the central image as the reference, we train a CNN to infer disparity representations pairing such image with frames on its left and right side. This strategy allows obtaining depth maps not affected by typical stereo artifacts. Moreover, being trinocular datasets seldom available, we introduce a novel interleaved training procedure enabling to enforce the trinocular assumption outlined from current binocular datasets. Exhaustive experimental results on the KITTI dataset confirm that our proposal outperforms state-of-the-art methods for unsupervised monocular depth estimation trained on binocular stereo pairs as well as any known methods relying on other cues., 14 pages, 7 figures, 4 tables. Accepted to 3DV 2018

Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively evaluated our proposal on 23 confidence measures, including 5 top-performing ones based on random-forests and CNNs, training our networks with two popular stereo algorithms and a small subset (25 out of 194 frames) of the KITTI 2012 dataset. Experimental results show that our approach dramatically increases the effectiveness of all the 23 confidence measures on the remaining frames. Moreover, without re-training, we report a further cross-evaluation on KITTI 2015 and Middlebury 2014 confirming that our proposal provides remarkable improvements for each confidence measure even when dealing with significantly different input data. To the best of our knowledge, this is the first method to move beyond conventional pixel-wise confidence estimation.

Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to improve stereo accuracy. This topic has been thoroughly investigated by Hu and Mordohai in 2010 (and 2012) considering 17 confidence measures and two local algorithms on the two datasets available at that time. However, since then major breakthroughs happened in this field: the availability of much larger and challenging datasets, novel and more effective stereo algorithms including ones based on deep learning and confidence measures leveraging on machine learning techniques. Therefore, this paper aims at providing an exhaustive and updated review and quantitative evaluation of 52 (actually, 76 considering variants) state-of-the-art confidence measures - focusing on recent ones mostly based on random-forests and deep learning - with three algorithms on the challenging datasets available today. Moreover we deal with problems inherently induced by learning-based confidence measures. How are these methods able to generalize to new data? How a specific training improves their effectiveness? How more effective confidence measures can actually improve the overall stereo accuracy?

Confidence measures aim at discriminating unreliable disparities inferred by a stereo vision system from reliable ones. A common and effective strategy adopted by most top-performing approaches consists in combining multiple confidence measures by means of an appropriately trained random-forest classifier. In this paper, we propose a novel approach by training an n-channel convolutional neural network on a set of feature maps, each one encoding the outcome of a single confidence measure. This strategy enables to move the confidence prediction problem from the conventional 1D feature maps domain, adopted by approaches based on random-forests, to a more distinctive 3D domain, going beyond single pixel analysis. This fact, coupled with a deep network appropriately trained on a small subset of images, enables to outperform top-performing approaches based on random-forests.

The advent of embedded stereo cameras based on low-power and compact devices such as FPGAs (Field Programmable Gate Arrays) has enabled to effectively address several computer vision problems. However, being the depth data generated by stereo algorithms affected by errors, reliable strategies to detect wrong disparity assignments by means of confidence measures are desirable. Recent works proved that confidence measures are also a powerful cue to improve the overall accuracy of stereo. Most approaches aimed at predicting match reliability rely on cost volume analysis, an information seldom available as output of most embedded depth sensors. Therefore, in this paper we analyze and evaluate strategies compatible with the constraints of embedded stereo cameras. In particular, we focus our attention on methods to infer match reliability inside depth sensors based on highly constrained computing architectures such as FPGAs. We quantitatively assess, on Middlebury 2014 and KITTI 2015 datasets, the impact of different design strategies for 16 confidence measures from the literature, suited for implementation on such embedded systems. Our evaluation shows that, compared to the confidence measures typically deployed in this context and based on storing intermediate results, other approaches yield much more accurate predictions with negligible computing requirements and memory footprint. This enables for their implementation even on highly constrained architectures.

Image distortion correction is a critical pre-processing step for a variety of computer vision and image processing algorithms. Standard real-time software implementations are generally not suited for direct hardware porting, so appropriated versions need to be designed in order to obtain implementations deployable on FPGAs. In this paper, hardware-compatible techniques for image distortion correction are introduced and analyzed in details. The considered solutions are compared in terms of output quality by using a geometrical-error-based approach, with particular emphasis on robustness with respect to increasing lens distortion. The required amount of hardware resources is also estimated for each considered approach., To be published in Proceedings of the International Conference on Reconfigurable Computing and FPGAs, Cancun, Mexico, 30 November, 2 December 2016

Inferring dense depth from stereo is crucial for several computer vision applications and stereo cameras based on embedded systems and/or reconfigurable devices such as FPGA became quite popular in the past years. In this field Semi Global Matching (SGM) is, in most cases, the preferred algorithm due to its good trade-off between accuracy and computation requirements. Nevertheless, a careful design of the processing pipeline enables significant improvements in terms of disparity map accuracy, hardware resources and frame rate. In particular factors like the amount of matching costs and parameters, such as the number/selection of scanlines, and so on have a great impact on the overall resource requirements. In this paper we evaluate different variants of the SGM algorithm suited for implementation on embedded or reconfigurable devices looking for the best compromise in terms of resource requirements, accuracy of the disparity estimation and running time. To assess quantitatively the effectiveness of the considered variants we adopt the KITTI 2015 training dataset, a challenging and standard benchmark with ground truth containing several realistic scenes.

Stereo matching is a popular technique to infer depth from two or more images and wealth of methods have been proposed to deal with this problem. Despite these efforts, finding accurate stereo correspondences is still an open problem. The strengths and weaknesses of existing methods are often complementary and in this paper, motivated by recent trends in this field, we exploit this fact by proposing Deep Stereo Fusion, a Convolutional Neural Network capable of combining the output of multiple stereo algorithms in order to obtain more accurate result with respect to each input disparity map. Deep Stereo Fusion process a 3D features vector, encoding both spatial and cross-algorithm information, in order to select the best disparity hypothesis among those proposed by the single stereo matchers. To the best of our knowledge, our proposal is the first i) to leverage on deep learning and ii) able to predict the optimal disparity assignments by taking only as input cue the disparity maps. This second feature makes our method suitable for deployment even when other cues (e.g., confidence) are not available such as when dealing with disparity maps provided by off-the-shelf 3D sensors. We thoroughly evaluate our proposal on the KITTI stereo benchmark with respect state-of-the-art in this field.

In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi-Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms.

Inferring dense depth from stereo is crucial for several computer vision applications and Semi Global Matching (SGM) is often the preferred choice due to its good tradeoff between accuracy and computation requirements. Nevertheless, it suffers of two major issues: streaking artifacts caused by the Scanline Optimization (SO) approach, at the core of this algorithm, may lead to inaccurate results and the high memory footprint that may become prohibitive with high resolution images or devices with constrained resources. In this paper, we propose a smart scanline aggregation approach for SGM aimed at dealing with both issues. In particular, the contribution of this paper is threefold: i) leveraging on machine learning, proposes a novel generalpurpose confidence measure suited for any for stereo algorithm, based on O(1) features, that outperforms state of-the-art ii) taking advantage of this confidence measure proposes a smart aggregation strategy for SGM enabling significant improvements with a very small overhead iii) the overall strategy drastically reduces the memory footprint of SGM and, at the same time, improves its effectiveness and execution time. We provide extensive experimental results, including a cross-validation with multiple datasets (KITTI 2012, KITTI 2015 and Middlebury 2014).