1,721,014 research outputs found
Stereo light probe
No full textIn this paper we present a practical, simple and robust method to acquire the spatially-varying illumination of a real-world scene. The basic idea of the proposed method is to acquire the radiance distribution of the scene using high-dynamic range images of two reflective balls. The use of two light probes instead of a single one allows to estimate, not only the direction and intensity of the light sources, but also the actual position in space of the light sources. To robustly achieve this goal we first rectify the two input spherical images, then, using a region-based stereo matching algorithm, we establish correspondences and compute the position of each light. The radiance distribution so obtained can be used for augmented reality applications, photo-realistic rendering and accurate reflectance properties estimation. The accuracy and the effectiveness of the method have been tested by measuring the computed light position and rendering synthetic version of a real object in the same scene. The comparison with standard method that uses a simple spherical lighting environment is also shown
Scanner 3D con hardware low cost e strumenti free/open source
No full text3D scanning technologies offer a lot of interesting possibilities in the Cultural Heritage. Unfortunately most of the current 3D scanning solutions has an high cost and need a significant budget investment, both in terms of software and hardware. This paper will discuss how perform 3D scanning acquisition of Cultural heritage objects using only low cost hardware and open source or free software tools
Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models
No full textThe technological advance of sensors is producing an exponential size growth of the data coming from 3D scanning and digital photography. The production of digital 3D models consisting of tens or even hundreds of millions of triangles is quite easy nowadays; at the same time, using high-resolution digital cameras it is also straightforward to produce a set of pictures of the same real object totalling more than 50M pixel. The problem is how to manage all this data to produce 3D models that could fit the interactive rendering constraints. A common approach is to go for mesh parametrization and texture synthesis, but finding a parametrization for such large meshes and managing such large textures can be prohibitive. Moreover, digital photo sampling produces highly redundant data; this redundancy should be eliminated while mapping to the 3D model but, at the same time, should also be efficiently used to improve the sampled data coherence and the appearance representation accuracy. In this paper we present an approach where a multivariate blending function weights all the available pixel data with respect to geometric, topological and colorimetric criteria. The blending approach proposed is efficient, since it mostly works independently on each image, and can be easily extended to include other image quality estimators. The resulting weighted pixels are then selectively mapped on the geometry. preferably by adopting a multiresolution per-vertex encoding to make profitable use of all the data available and to avoid the texture size bottleneck. Some practical examples on complex data sets are presented. (C) 2008 Elsevier Ltd. All rights reserved
SEMANTIC SEGMENTATION of BENTHIC COMMUNITIES from ORTHO-MOSAIC MAPS
Full text linkVisual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes.Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expertoperator remains, in this context, a very time consuming task. It has been estimated that only the 1-2%of the acquired images are lateranalyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectivelyexploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in thisfield, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data.In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segmentthe investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although theuse of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number ofdifferent species, making the procedure of marine organism identification an highly adaptable tas
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