DSpace Repository :: Browsing by Author "Pintus, Ruggero"

Browsing by Author "Pintus, Ruggero"

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Aging Prediction of Cultural Heritage Samples Based on Surface Microgeometry
(The Eurographics Association, 2018) Ciortan, Irina Mihaela; Marchioro, Giacomo; Daffara, Claudia; Pintus, Ruggero; Gobbetti, Enrico; Giachetti, Andrea; Sablatnig, Robert and Wimmer, Michael
A critical and challenging aspect for the study of Cultural Heritage (CH) assets is related to the characterization of the materials that compose them and to the variation of these materials with time. In this paper, we exploit a realistic dataset of artificially aged metallic samples treated with different coatings commonly used for artworks' protection in order to evaluate different approaches to extract material features from high-resolution depth maps. In particular, we estimated, on microprofilometric surface acquisitions of the samples, performed at different aging steps, standard roughness descriptors used in materials science as well as classical and recent image texture descriptors. We analyzed the ability of the features to discriminate different aging steps and performed supervised classification tests showing the feasibility of a texture-based aging analysis and the effectiveness of coatings in reducing the surfaces' change with time.
Applying BRDF Monotonicity for Refined Shading Normal Extraction from Multi-Light Image Collections
(The Eurographics Association, 2024) Pintus, Ruggero; Zorcolo, Antonio; Gobbetti, Enrico; Corsini, Massimiliano; Ferdani, Daniele; Kuijper, Arjan; Kutlu, Hasan
Multi-Light Image Collections (MLICs) are often transformed into geometric normals and BRDF normals for visual exploration under novel illumination. However, discrepancies between the chosen BRDF space and the complete optical behavior of objects, along with the possible presence of non-local lighting effects in measurements, often lead to sub-optimal visual outcomes even with the most accurate geometric normal recovery. In this paper, we introduce a modular component designed to convert the geometric normals into well-behaved shading normals, under the common and general assumption that the reflectance must be a monotonic function of the angle between the shading normal and the bisector of lighting and viewing directions. Since it does not require the coupling of shape and material estimation, the module allows seamless integration into existing reconstruction pipelines, supporting the mixing and matching of Photometric Stereo methods, BRDF models, and BRDF fitters. The performance and versatility of the approach are demonstrated through experiments.
Crack Detection in Single- and Multi-Light Images of Painted Surfaces using Convolutional Neural Networks
(The Eurographics Association, 2019) Dulecha, Tinsae Gebrechristos; Giachetti, Andrea; Pintus, Ruggero; Ciortan, Irina; Villanueva, Alberto Jaspe; Gobbetti, Enrico; Rizvic, Selma and Rodriguez Echavarria, Karina
Cracks represent an imminent danger for painted surfaces that needs to be alerted before degenerating into more severe aging effects, such as color loss. Automatic detection of cracks from painted surfaces' images would be therefore extremely useful for art conservators; however, classical image processing solutions are not effective to detect them, distinguish them from other lines or surface characteristics. A possible solution to improve the quality of crack detection exploits Multi-Light Image Collections (MLIC), that are often acquired in the Cultural Heritage domain thanks to the diffusion of the Reflectance Transformation Imaging (RTI) technique, allowing a low cost and rich digitization of artworks' surfaces. In this paper, we propose a pipeline for the detection of crack on egg-tempera paintings from multi-light image acquisitions and that can be used as well on single images. The method is based on single or multi-light edge detection and on a custom Convolutional Neural Network able to classify image patches around edge points as crack or non-crack, trained on RTI data. The pipeline is able to classify regions with cracks with good accuracy when applied on MLIC. Used on single images, it can give still reasonable results. The analysis of the performances for different lighting directions also reveals optimal lighting directions.
Disk-NeuralRTI: Optimized NeuralRTI Relighting through Knowledge Distillation
(The Eurographics Association, 2024) Dulecha, Tinsae Gebrechristos; Righetto, Leonardo; Pintus, Ruggero; Gobbetti, Enrico; Giachetti, Andrea; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
Relightable images created from Multi-Light Image Collections (MLICs) are among the most employed models for interactive object exploration in cultural heritage (CH). In recent years, neural representations have been shown to produce higherquality images at similar storage costs to the more classic analytical models such as Polynomial Texture Maps (PTM) or Hemispherical Harmonics (HSH). However, the Neural RTI models proposed in the literature perform the image relighting with decoder networks with a high number of parameters, making decoding slower than for classical methods. Despite recent efforts targeting model reduction and multi-resolution adaptive rendering, exploring high-resolution images, especially on high-pixelcount displays, still requires significant resources and is only achievable through progressive rendering in typical setups. In this work, we show how, by using knowledge distillation from an original (teacher) Neural RTI network, it is possible to create a more efficient RTI decoder (student network). We evaluated the performance of the network compression approach on existing RTI relighting benchmarks, including both synthetic and real datasets, and on novel acquisitions of high-resolution images. Experimental results show that we can keep the student prediction close to the teacher with up to 80% parameter reduction and almost ten times faster rendering when embedded in an online viewer.
Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs
(The Eurographics Association, 2021) Pintus, Ruggero; Ahsan, Moonisa; Marton, Fabio; Gobbetti, Enrico; Hulusic, Vedad and Chalmers, Alan
We present a practical solution to create a relightable model from Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking few images with a different local illumination. By exploiting information from neighboring pixels through a carefully crafted weighting and regularization scheme, we are able to efficiently infer subtle per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method is qualitatively and quantitatively evaluated on both synthetic data and real paintings in the scope of image-based relighting applications.
GCH 2022: Frontmatter
(The Eurographics Association, 2022) Ponchio, Federico; Pintus, Ruggero; Ponchio, Federico; Pintus, Ruggero
Guiding Lens-based Exploration using Annotation Graphs
(The Eurographics Association, 2021) Ahsan, Moonisa; Marton, Fabio; Pintus, Ruggero; Gobbetti, Enrico; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
We introduce a novel approach for guiding users in the exploration of annotated 2D models using interactive visualization lenses. Information on the interesting areas of the model is encoded in an annotation graph generated at authoring time. Each graph node contains an annotation, in the form of a visual markup of the area of interest, as well as the optimal lens parameters that should be used to explore the annotated area and a scalar representing the annotation importance. Graph edges are used, instead, to represent preferred ordering relations in the presentation of annotations. A scalar associated to each edge determines the strength of this prescription. At run-time, the graph is exploited to assist users in their navigation by determining the next best annotation in the database and moving the lens towards it when the user releases interactive control. The selection is based on the current view and lens parameters, the graph content and structure, and the navigation history. This approach supports the seamless blending of an automatic tour of the data with interactive lens-based exploration. The approach is tested and discussed in the context of the exploration of multi-layer relightable models.
Objective and Subjective Evaluation of Virtual Relighting from Reflectance Transformation Imaging Data
(The Eurographics Association, 2018) Pintus, Ruggero; Dulecha, Tinsae; Jaspe, Alberto; Giachetti, Andrea; Ciortan, Irina; Gobbetti, Enrico; Sablatnig, Robert and Wimmer, Michael
Reflectance Transformation Imaging (RTI) is widely used to produce relightable models from multi-light image collections. These models are used for a variety of tasks in the Cultural Heritage field. In this work, we carry out an objective and subjective evaluation of RTI data visualization. We start from the acquisition of a series of objects with different geometry and appearance characteristics using a common dome-based configuration. We then transform the acquired data into relightable representations using different approaches: PTM, HSH, and RBF. We then perform an objective error estimation by comparing ground truth images with relighted ones in a leave-one-out framework using PSNR and SSIM error metrics. Moreover, we carry out a subjective investigation through perceptual experiments involving end users with a variety of backgrounds. Objective and subjective tests are shown to behave consistently, and significant differences are found between the various methods. While the proposed analysis has been performed on three common and state-of-the-art RTI visualization methods, our approach is general enough to be extended and applied in the future to new developed multi-light processing pipelines and rendering solutions, to assess their numerical precision and accuracy, and their perceptual visual quality.
Recovering 3D Indoor Floor Plans by Exploiting Low-cost Spherical Photography
(The Eurographics Association, 2018) Pintore, Giovanni; Ganovelli, Fabio; Pintus, Ruggero; Scopigno, Roberto; Gobbetti, Enrico; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a novel approach to automatically recover, from a small set of partially overlapping panoramic images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. Our improvements over previous approaches include a new method for geometric context extraction based on a 3D facets representation, which combines color distribution analysis of individual images with sparse multi-view clues, as well as an efficient method to combine the facets from different point-of-view in the same world space, considering the reliability of the facets contribution. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments where most of the other previous approaches fail, such as in presence of hidden corners, large clutter and sloped ceilings, even without involving additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes.
Smart Tools and Applications in computer Graphics: Frontmatter
(The Eurographics Association, 2020) Biasotti, Silvia; Pintus, Ruggero; Berretti, Stefano; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
STAG 2019: Frontmatter
(Eurographics Association, 2019) Agus, Marco; Corsini, Massimiliano; Pintus, Ruggero; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
State-of-the-art in Multi-Light Image Collections for Surface Visualization and Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Pintus, Ruggero; Dulecha, Tinsae Gebrechristos; Ciortan, Irina Mihaela; Gobbetti, Enrico; Giachetti, Andrea; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Multi-Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up-to-date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.
SynthPS: a Benchmark for Evaluation of Photometric Stereo Algorithms for Cultural Heritage Applications
(The Eurographics Association, 2020) Dulecha, Tinsae Gebrechristos; Pintus, Ruggero; Gobbetti, Enrico; Giachetti, Andrea; Spagnuolo, Michela and Melero, Francisco Javier
Photometric Stereo (PS) is a technique for estimating surface normals from a collection of images captured from a fixed viewpoint and with variable lighting. Over the years, several methods have been proposed for the task, trying to cope with different materials, lights, and camera calibration issues. An accurate evaluation and selection of the best PS methods for different materials and acquisition setups is a fundamental step for the accurate quantitative reconstruction of objects' shapes. In particular, it would boost quantitative reconstruction in the Cultural Heritage domain, where a large amount of Multi-Light Image Collections are captured with light domes or handheld Reflectance Transformation Imaging protocols. However, the lack of benchmarks specifically designed for this goal makes it difficult to compare the available methods and choose the most suitable technique for practical applications. An ideal benchmark should enable the evaluation of the quality of the reconstructed normals on the kind of surfaces typically captured in real-world applications, possibly evaluating performance variability as a function of material properties, light distribution, and image quality. The evaluation should not depend on light and camera calibration issues. In this paper, we propose a benchmark of this kind, SynthPS, which includes synthetic, physically-based renderings of Cultural Heritage object models with different assigned materials. SynthPS allowed us to evaluate the performance of classical, robust and learning-based Photometric Stereo approaches on different materials with different light distributions, also analyzing their robustness against errors typically arising in practical acquisition settings, including robustness against gamma correction and light calibration errors.
Web-based Multi-layered Exploration of Annotated Image-based Shape and Material Models
(The Eurographics Association, 2019) Villanueva, Alberto Jaspe; Pintus, Ruggero; Giachetti, Andrea; Gobbetti, Enrico; Rizvic, Selma and Rodriguez Echavarria, Karina
We introduce a novel versatile approach for letting users explore detailed image-based shape and material models integrated with structured, spatially-associated descriptive information. We represent the objects of interest as a series of registered layers of image-based shape and material information. These layers are represented at multiple scales, and can come out of a variety of pipelines and include both RTI representations and spatially-varying normal and BRDF fields, eventually as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer can be easily authored at data preparation time using widely available image editing tools. At run-time, an annotated multi-layered dataset is made available to clients by a standard web server. Users can explore these datasets on a variety of devices, from mobile phones to large scale displays in museum installations, using JavaScript/WebGL2 clients capable to perform layer selection, interactive relighting and enhanced visualization, annotation display, and focus-and-context multiple-layer exploration using a lens metaphor. The capabilities of our approach are demonstrated on a variety of cultural heritage use cases involving different kinds of annotated surface and material models.

Browsing by Author "Pintus, Ruggero"

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