3D scanning in cultural heritage (CH) is used in most cases either for the faithful generation of digital models of CH objects or for visualization purposes. In this paper, we move a step further and concentrate on documenting the requirements and our experience in 3D scanning for alternative CH application scenarios, where digitization is not the end product, but rather the means to augment the existing information and acquired data. In our work, which is part of the PRESIOUS EU-funded project, we aim at utilizing and inventing new methodologies and technologies for the prediction of geometric information on CH data, ranging from the digitization process itself to geometric reassembly, shape prediction and simulation/prediction of monument degradation. To this end, the scanning requirements of the different processing tasks are given, including specialized high-definition scans for erosion measurement, meso-scale digitization for reassembly as well as for the visualization of the results. Important practical lessons are drawn and the actual digitisation pipelines of state-of-art 3D digitisation technologies are given. A practical discussion summarizes our multi-scale digitisation experience (giving the accuracy, required time and resulting data size that we observed), mainly drawn from the digitization activities at the Nidaros Cathedral, Trondheim, Norway.
Partial shape retrieval is a challenging problem in content-based 3D model retrieval. This track intends to evaluate the performance of existing algorithms for partial retrieval. The contest is based on a new large-scale query set obtained by mimicking the range image acquisition using a standard 3D benchmark as target set. The query set contains 7200 partial meshes with different levels of complexity. Furthermore, we propose the use of new performance measures based on a partiality factor. With this characteristics, our goal is to evaluate several important aspects: effectiveness, efficiency, robustness and scalability. The obtained results of this track open new questions regarding the difficulty of the partial shape retrieval problem and the scalability of algorithms. In addition,potential future directions on this topic are identified.
In this paper, we present a new approach for generic 3D shape retrieval based on a mesh partitioning scheme. Our method combines a mesh global description and mesh partition descriptions to represent a 3D shape. The partitioning is useful because it helps us to extract additional information in a more local sense. Thus, part descriptions can mitigate the semantic gap imposed by global description methods. We propose to ﬁnd spatial agglomerations of local features to generate mesh partitions. Hence, the deﬁnition of a distance function is stated as an optimization problem to ﬁnd the best match between two shape representations. We show that mesh partitions are representative and therefore it helps to improve the effectiveness in retrieval tasks. We present exhaustive experimentation using the SHREC'09 Generic Shape Retrieval Benchmark.
Screen-space ambient occlusion and obscurance (AO) techniques have become de-facto methods for ambient light attenuation and contact shadows in real-time rendering. Although extensive research has been conducted to improve the quality and performance of AO techniques, view-dependent artifacts remain a major issue. This paper introduces Multi-view Ambient Occlusion, a generic per-fragment view weighting scheme for evaluating screen-space occlusion or obscurance using multiple, arbitrary views, such as the readily available shadow maps. Additionally, it exploits the resulting weights to perform adaptive sampling, based on the importance of each view to reduce the total number of samples, while maintaining the image quality. Multi-view Ambient Occlusion improves and stabilizes the screen-space AO estimation without overestimating the results and can be combined with a variety of existing screenspace AO techniques. We demonstrate the results of our sampling method with both open volume- and solid angle-based AO algorithms.