Reflectance Transformation Imaging (RTI) is a computational photography technique in which an object is acquired from a fixed point-of-view with different light directions. The aim is to estimate the light transport function at each point so that the object can be interactively relighted in a physically-accurate way, revealing its surface characteristics. In this paper, we propose a novel RTI approach describing surface reflectance as an implicit neural representation acting as a ”relightable image” for a specific object. We propose to represent the light transport function with a Neural Reflectance Field (NRF) model, feeding it with pixel coordinates, light direction, and a latent vector encoding the per-pixel reflectance in a neighbourhood. These vectors, computed during training, allow a more accurate relighting than a pure implicit representation (i.e., relying only on positional encoding) enabling the NRF to handle complex surface shadings. Moreover, they can be efficiently stored with the learned NRF for compression and transmission. As an additional contribution, we propose a novel synthetic dataset containing objects of various shapes and materials created with a physically based rendering software. An extensive experimental section shows that the proposed NRF accurately models the light transport function for challenging datasets in synthetic and real-world scenarios.

Whitecaps are formed at the ocean surface when breaking waves entrain sufficient quantities of air to produce an optically distinct signal at the water surface. Whitecaps drive energy transfer from the surface wave field to the upper ocean and the entrained air drives key bubble-mediated processes such as air-sea gas exchange and aerosol production flux. By exploiting the broadband scattering of light by the surface whitecaps, this study develops an algorithm for automated whitecap detection and tracking (AWDAT) from fixed image systems, in order to detect and track individual whitecaps. AWDAT introduces new image processing and computer vision techniques that handle temporal developments of whitecaps and complex behaviors of whitecap foam patches such as splitting and merging. We teach a learning-based model to filter tracked whitecaps based on the AWDAT-measured foam area, breaking speed, and direction time series. The algorithm is tested on three different image datasets to assess its performance with different camera systems in different geographic locations—the Adriatic Sea (AS), Black Sea (BS), and Yellow Sea (YS). Applications of AWDAT are demonstrated by aggregating whitecap statistics from geometric, kinematic, and dynamic measurements of individual breaking waves, which are then evaluated within the volume-time-integral (VTI) method and Phillips (1985)$\Lambda (c)$spectral framework.

Structured-light 3D reconstruction techniques are employed in a wide range of applications for industrial inspection. In particular, some tasks require micrometric precision for the identification of microscopic surface irregularities. We propose a novel calibration technique for structured-light systems adopting telecentric lenses for both camera and projector. The device exploits a fixed light pattern (striped-based) to perform accurate microscopic surface reconstruction and measurements. Our method employs a sphere with a known radius as calibration target and takes advantage of the orthographic projection model of the telecentric lenses to recover the bundle of planes originated by the projector. Once the sheaf of parallel planes is properly described in the camera reference frame, the triangulation of the surface’s object hit by the light stripes is immediate. Moreover, we tested our technique in a real-world scenario for industrial surface inspection by implementing a complete pipeline to recover the intersections between the projected planes and the surface. Experimental analysis shows the robustness of the proposed approach against synthetic and real-world test data.

Industrial manufacturing processes often involve a visual control system to detect possible product defects during production. Such inspection devices usually include one or more cameras and several light sources designed to highlight surface imperfections under different illumination conditions (e.g. bumps, scratches, holes). In such scenarios, a preliminary calibration procedure of each component is a mandatory step to recover the system’s geometrical configuration and thus ensure a good process accuracy. In this paper we propose a procedure to estimate the position of each light source with respect to a camera network using an inexpensive Lambertian spherical target. For each light source, the target is acquired at different positions from different cameras, and an initial guess of the corresponding light vector is recovered from the analysis of the collected intensity isocurves. Then, an energy minimization process based on the Lambertian shading model refines the result for a pr ecise 3D localization. We tested our approach in an industrial setup, performing extensive experiments on synthetic and real-world data to demonstrate the accuracy of the proposed approach.

Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of the PFA and the characteristics of the imaged scene. Non-linear effects, like the cross-talk among neighbouring pixels, are difficult to explicitly model and suggest the potential advantage of a data-driven learning approach. However, the PFA cannot be removed from the sensor, making it difficult to acquire the ground-truth polarization state for training. In this work we propose a novel CNN-based model which directly demosaics the raw camera image to a per-pixel Stokes vector. Our contribution is twofold. First, we propose a network architecture composed by a sequence ofMosaiced Convolutionsoperating coherently with the local arrangement of the different filters. Second, we introduce a new method, employing a consumer LCD screen, to effectively acquire real-world data for training. The process is designed to be invariant by monitor gamma and external lighting conditions. We extensively compared our method against algorithmic and learning-based demosaicing techniques, obtaining a consistently lower error especially in terms of polarisation angle.

Phase-shift is one of the most effective techniques in 3D structured-light scanning for its accuracy and noise resilience. However, the periodic nature of the signal causes a spatial ambiguity when the fringe periods are shorter than the projector resolution. To solve this, many techniques exploit multiple combined signals to unwrap the phases and thus recovering a unique consistent code. In this paper, we study the phase estimation and unwrapping problem in a stochastic context. Assuming the acquired fringe signal to be affected by additive white Gaussian noise, we start by modelling each estimated phase as a zero-mean Wrapped Normal distribution with variance σ ̄ 2 . Then, our contributions are twofolds. First, we show how to recover the best projector code given multiple phase observations by means of a ML estimation over the combined fringe distributions. Second, we exploit the Cramér-Rao bounds to relate the phase variance σ ̄ 2 to the variance of the observed signal, that can be easily estimated online during the fringe acquisition. An extensive set of experiments demonstrate that our approach outperforms other methods in terms of code recovery accuracy and ratio of faulty unwrappings.

The ubiquitous presence of cylindrical shapes in both natural and man-made environments makes their automated extraction a pivotal task for a broad range of applications such as robot manipulation, reverse engineering and automated industrial inspection. Albeit conceptually simple, the task of fitting cylinders from 3D data can quickly become challenging if performed "in-the-wild", when no prior is given to the number of primitives to find or when the point cloud is noisy and not oriented. In this paper we introduce a new robust approach to iteratively extract cylindrical primitives from a 3D point cloud by exploiting mutual consensus of different cylinder candidates. First, a set of possible axes is generated by slicing the point cloud with multiple random planes. Then, a game-theoretic inlier selection process is performed to extract a subset of axes maximizing the fitness against a payoff function based on the shortest geodesic path in SE(3) between pairs of corresponding 3D lines. Finally, the probability distribution resulting from the previous selection step is used to weight the input candidates and robustly obtain the final cylinder coefficients. Compared to other methods, our approach does not require point normals, offers superior resilience to noise and does not depend on delicate tuning of multiple parameters.

Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narrow datasets or with a poor range of different samples. However, the benefits of data augmentation are abysmal when applied to a dataset which is inherently unable to cover all the categories to be classified with a significant number of samples. To deal with such desperate scenarios, we propose a possible last resort: Cross-Dataset Data Augmentation. That is, the creation of new samples by morphing observations from a different source into credible specimens for the training dataset. Of course specific and strict conditions must be satisfied for this trick to work. In this paper we propose a general set of strategies and rules for Cross-Dataset Data Augmentation and we demonstrate its feasibility over a concrete case study. Even without defining any new formal approach, we think that the preliminary results of our paper are worth to produce a broader discussion on this topic.

Among several structured light approaches, phase shift is the most widely adopted in real-world 3D reconstruction devices. This is mainly due to its high accuracy, strong resilience to noise and straightforward implementation. However, Phase shift also exhibits an inherent weakness, that is the spatial ambiguity resulting from the periodicity of the sinusoidal wave adopted. Of course many phase unwrapping methods have been proposed to solve such ambiguity. One of the most promising methods exploits additional signals of mutually prime periods, in order to observe a distinct combination of phases for each spatial point. Unfortunately, for such combination to be properly recognized, a very high accuracy in phase recovery must be attained for each signal. In fact, even modest errors could lead to unwrapping faults, making the overall approach much less resilient to noise than plain phase shift. With this paper we introduce a feasible and effective fault recovery method that can be directly applied to multi-period phase shift. The combined pipeline offers an optimal accuracy and coverage even with high noise conditions, overcoming the setbacks of the original method. The performance of such pipeline is established by means of an in depth set of experimental evaluations and comparison, both with real and synthetically generated data.

Among structured light strategies, the ones based on phase shift are considered to be the most adaptive with respect to the features of the objects to be captured. Inter alia, the theoretical invariance to signal strength and the absence of discontinuities in intensity, make phase shift an ideal candidate to deal with complex surfaces of unknown geometry, color and texture. However, in practical scenarios, unexpected artifacts could still result due to the characteristics of real cameras. This is the case, for instance, with high contrast areas resulting from abrupt changes in the albedo of the captured objects. In fact, the not negligible size of pixels and the presence of blur can produce a mix of signal integration from adjacent areas with different albedo. This, in turn, would result in a bias in the phase recovery and, consequentially, in an inaccurate 3D reconstruction of the surface. While this problem affects most structure light methods based on phase shift or derived techniques, little effort has been put in addressing it. With this paper we propose a model for the phase corruption and a theoretically sound correction step to be adopted to compensate the bias. The practical effectiveness of our approach is well demonstrated by a complete set of experimental evaluations.

At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process. To this end, the pinhole model with radial distortion is probably the most commonly used, as it balances low complexity with a precision that is sufficient for most applications. On the other hand, unconstrained non-parametric models, despite being originally proposed to handle specialty cameras, have been shown to outperform the pinhole model, even with the simpler setups. Still, notwithstanding the higher accuracy, the inability of describing the imaging model by simple linear projective operators severely limits the use of standard algorithms with unconstrained models. In this paper we propose a parameter-free camera model where each imaging ray is constrained to a common optical center, forcing the camera to be central. Such model can be easily calibrated with a practical procedure which provides a convenient undistortion map that can be used to obtain a virtual pinhole camera. The proposed method can also be used to calibrate a stereo rig with a displacement map that simultaneously provides stereo rectification and corrects lens distortion.

When a planar structure is observed from multiple views, the projections of its corresponding 3D points on each image are related by a homography. Its estimation is a key step in many computer vision tasks where either the rigid motion between views or a per-pixel image correspondence is sought. The vast majority of multi-view homography estimation techniques relies on matching a sparse set of point-to-point correspondences to establish a connected graph in the camera network. This track creation step is critical to ensure that the following bundle adjustment can estimate a globally optimal alignment in which the error is diffused coherently on each pairwise homography. On the other hand, erroneous or short tracks often cause misalignments among the views. We propose an optimization technique to simultaneously recover a transitively consistent network of planar homographies between multiple views together with a segmentation of the pixels comprising the observed plane (Fig. 1). Our method acts on a per-pixel basis to avoid a preliminary multi-view sparse feature matching step. Similarly to bundle adjustment, the error is diffused so that each homography in the view graph is transitively consistent with the others. The effectiveness of the proposed approach is evaluated in real-world scenarios and synthetically generated scenes.

Feature matching is a key step in most Computer Vision tasks involving several views of the same subject. In fact, it plays a crucial role for a successful reconstruction of 3D information of the corresponding material points. Typical approaches to construct stable feature tracks throughout a sequence of images operate via a two-step process: First, feature matches are extracted among all pairs of points of view; these matches are then given in input to a regularizer that provides a final, globally consistent solution. In this paper, we formulate this matching problem as a simultaneous optimization over the entire image collection, without requiring previously computed pairwise matches to be given as input. As our formulation operates directly in the space of feature across multiple images, the final matches are consistent by construction. Our matching problem has a natural interpretation as a non-cooperative game, which allows us to leverage tools and results from Game Theory. We performed a specially crafted set of experiments demonstrating that our approach compares favorably with the state of the art, while retaining a high computational efficiency.

A number of critical factors arises when a complex 3D scene is to be reconstructed by means of a large sequence of different views. Some of them are related to the ability to recover the correct identity and the accurate projection of each observed feature. Other sources of error are tied to the reliability of the orientation estimate for each view. With this paper we propose a method that tries to solve both problems at the same time, while being also inherently resilient to outliers. At the core of the approach stands a widely adopted game-theoretical selection technique, which has already been successfully embraced to address similar tasks. The original inception, however, has been further refined to address a wider range of scenarios, as well as to offer a reduced memory consumption and computation complexity. By exploiting these enhancements, we were able to apply our technique to a large-scale setup involving several hundreds of view points and tens of thousands of independent observations.

Artificial markers are successfully adopted to solve several vision tasks, ranging from tracking to calibration. While most designs share the same working principles, many specialized approaches exist to address specific application domains. Some are specially crafted to boost pose recovery accuracy. Others are made robust to occlusion or easy to detect with minimal computational resources. The sheer amount of approaches available in recent literature is indeed a statement to the fact that no silver bullet exists. Furthermore, this is also a hint to the level of scholarly interest that still characterizes this research topic. With this paper we try to add a novel option to the offer, by introducing a general purpose fiducial marker which exhibits many useful properties while being easy to implement and fast to detect. The key ideas underlying our approach are three. The first one is to exploit the projective invariance of conics to jointly find the marker and set a reading frame for it. Moreover, the tag identity is assessed by a redundant cyclic coded sequence implemented using the same circular features used for detection. Finally, the specific design and feature organization of the marker are well suited for several practical tasks, ranging from camera calibration to information payload delivery.

Given the raising interest in light-field technology and the increasing availability of professional devices, a feasible and accurate calibration method is paramount to unleash practical applications. In this paper we propose to embrace a fully non-parametric model for the imaging and we show that it can be properly calibrated with little effort using a dense active target. This process produces a dense set of independent rays that cannot be directly used to produce a conventional image. However, they are an ideal tool for 3D reconstruction tasks, since they are highly redundant, very accurate and they cover a wide range of different baselines. The feasibility and convenience of the process and the accuracy of the obtained calibration are comprehensively evaluated through several experiments.

In many visual interaction applications the user needs to explore a scene by moving with respect to the virtual environment. Using a fixed camera viewpoint leads to visual inconsistencies, which can be avoided only if the exact pose of the user head is known and can be used to produce a perspective correct rendering. To this end, tracking devices are often used, however many of them are relatively expensive or require the user to wear special apparel. With this paper we present a tracking system that can be implemented with a simple and very low cost modification of standard shutter glasses. The accuracy of such approach has been evaluated quantitatively with a specially crafted experimental setup.

Many 3D scanning techniques rely on two or more well calibrated imaging cameras and a structured light source. Within these setups the light source does not need any calibration. In fact the shape of the target surface can be inferred by the cameras geometry alone, while the structured light is only exploited to establish stereo correspondences. Unfortunately, this approach requires each reconstructed point to exhibit an unobstructed line of sight from three independent points of views. This requirement limits the amount of scene points that can be effectively captured with each shot. To overcome this restriction, several systems that combine a single camera with a calibrated projector have been proposed. However, this type of calibration is more complex to be performed and its accuracy is hindered by both the indirect measures involved and the lower precision of projector optics. In this paper we propose an online calibration method for structured light sources that computes the projector parameters concurrently with regular scanning shots. This results in an easier and seamless process that can be applied directly to most current scanning systems without modification. Moreover, we attain high accuracy by adopting an unconstrained imaging model that is able to handle well even less accurate optics. The improved surface coverage and the quality of the measurements are thoroughly assessed in the experimental section.

The estimation of camera intrinsic parameters plays a crucial role in all computer vision tasks for which the underlying model that drives the image formation process has to be known. As a consequence, a deluge set of different approaches has been proposed in literature over the last decades. Most of those lean on the observation of a known object (i.e. a calibration target) from different point of views, providing the necessary data to estimate the model through different optimization approaches. In this work, we exploit the projective properties of conics to estimate the focal length and optical center of a pinhole camera just by observing a set of coplanar circles, where neither the radius nor the reciprocal position of each circle has to be known a-priori. This make such method particularly interesting whenever the usage of a calibration target is not a feasible option. Our contribution is twofold. First, we propose a reliable method to locate coplanar circles from images by means of a non-cooperative evolutionary game. Second, we refine the estimation of camera parameters with a non-linear function minimization through a simple yet effective gradient descent. Performance of the proposed approach is assessed through an experimental section consisting on both quantitative and qualitative tests.

Traditional stereoscopic displays assume the viewer to be standing at a specific location, that is the same pose (relative to the screen) of the stereo camera pair that depicted the scene (physically or virtually). Even for basic applications, such as movies or games, this leads to visual inconsistencies as soon as the user moves his head. Moreover, with this premises, it is not possible at all to develop more sophisticated 3D applications, involving the freedom for the user to walk around objects or to interact with them. The most popular solution to these limitation is to track the user head in order to produce a scene rendering that can be seen correctly from his point of view. With this paper we propose a viewer-dependent system that is very easy to implement since it is based on a simple augmentation of basic shutter glasses used in standard stereoscopic setups. Furthermore, we introduce a practical and sound method to quantitatively assess the accuracy of any view-dependent display approach. This fills a clear shortcoming of the currently adopted evaluation methods, that are for the most part qualitative.

Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process. These considerations led to the definition of several ad hoc models that best adapt to different imaging devices, ranging from pinhole cameras with no radial distortion to the more complex catadioptric or polydioptric optics. In this paper we propose the use of an unconstrained model even in standard central camera settings dominated by the pinhole model, and introduce a novel calibration approach that can deal effectively with the huge number of free parameters associated with it, resulting in a higher precision calibration than what is possible with the standard pinhole model with correction for radial distortion. This effectively extends the use of general models to settings that traditionally have been ruled by parametric approaches out of practical considerations. The benefit of such an unconstrained model to quasi-pinhole central cameras is supported by an extensive experimental validation.

We consider the problem of minimum distortion intrinsic correspondence between deformable shapes, many useful formulations of which give rise to the NP-hard quadratic assignment problem (QAP). Previous attempts to use the spectral relaxation have had limited success due to the lack of sparsity of the obtained “fuzzy” solution. In this paper, we adopt the recently introduced alternative L1relaxation of the QAP based on the principles of game theory. We relate it to the Gromov and Lipschitz metrics between metric spaces and demonstrate on state-of-the-art benchmarks that the proposed approach is capable of finding very accurate sparse correspondences between deformable shapes.

Over the last decades fiducial markers have provided widely adopted tools to add reliable model-based features into an otherwise general scene. Given their central role in many computer vision tasks, countless different solutions have been proposed in the literature. Some designs are focused on the accuracy of the recovered camera pose with respect to the tag; some other concentrate on reaching high detection speed or on recognizing a large number of distinct markers in the scene. In such a crowded area both the researcher and the practitioner are licensed to wonder if there is any need to introduce yet another approach. Nevertheless, with this paper, we would like to present a general purpose fiducial marker system that can be deemed to add some valuable features to the pack. Specifically, by exploiting the projective properties of a circular set of sizeable dots, we propose a detection algorithm that is highly accurate. Further, applying a dot pattern scheme derived from error-correcting codes, allows for robustness with respect to very large occlusions. In addition, the design of the marker itself is flexible enough to accommodate different requirements in terms of pose accuracy and number of patterns. The overall performance of the marker system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented.