This paper gives an overview on crowdsourcing practices in virtual museums. Engaged nonprofessionals and specialists support curators in creating digital 2D or 3D exhibits, exhibitions and tour planning and enhancement of metadata using the Virtual Museum and Cultural Object Exchange Format (ViMCOX). ViMCOX provides the semantic structure of exhibitions and complete museums and includes new features, such as room and outdoor design, interactions with artwork, path planning and dissemination and presentation of contents. Application examples show the impact of crowdsourcing in the Museo de Arte Contemporaneo in Santiago de Chile and in the virtual museum depicting the life and work of the Jewish sculptor Leopold Fleischhacker. A further use case is devoted to crowd-based support for restoration of high-quality 3D shapes.
The weathering experiment reported is part of a broader project that, among other aims, investigate innovative ICT solutions to quantify and characterize stone monument degradations. As an output, models would be developed for forward and inverse weathering prediction based on targeted high-accuracy surface scans and accelerated weathering experiments. The salt weathering experiment, along with similar experiments on other factors of deterioration, would help to gain insight into the deterioration processes at microscopic and macroscopic levels. The results would be used to better explain observed deteriorations\weathering and, thus, their prediction.
A common approach for implementing contentbased multimedia retrieval tasks resorts to extracting highdimensional feature vectors from the multimedia objects. In combination with an appropriate dissimilarity function, such as the well-known Lp functions or statistical measures like χ 2 , one can rank objects by dissimilarity with respect to a query. For many multimedia retrieval problems, a large number of feature extraction methods have been proposed and experimentally evaluated for their effectiveness. Much less work has been done to systematically study the impact of the choice of dissimilarity function on the retrieval effectiveness.
Inspired by previous work which compared dissimilarity functions for image retrieval, we provide an extensive comparison of dissimilarity measures for 3D object retrieval. Our study is based on an encompassing set of feature extractors, dissimilarity measures and benchmark data sets. We identify the best performing dissimilarity measures and in turn identify dependencies between well-performing dissimilarity measures and types of 3D features. Based on these findings, we show that the effectiveness of 3D retrieval can be improved by a feature-dependent measure choice. In addition, we apply different normalization schemes to the dissimilarity distributions in order to show improved retrieval effectiveness for late fusion of multi-feature combination. Finally, we present preliminary findings on the correlation of rankings for dissimilarity measures, which could be exploited for further improvement of retrieval effectiveness for single features as well as combinations.
We present a novel and generic user-guided approach for the digital reconstruction of cultural heritage finds from fragments, which operates directly on generic 3D objects. Central to our approach is a three-tier geometric registration approach that addresses the reassembly problem using i) the contact surface of the fractured objects, ii) feature curves on the intact surfaces and iii) partial object symmetries. In contrast to most existing methodologies, our approach is more generic and addresses even the most difficult cases, where contact surface is unusable, small or absent. We evaluate our method using digitized fragments from the Nidaros Cathedral.
The problem of object restoration from eroded fragments, where large parts could be missing, is of high relevance in archaeology. Manual restoration is possible and common in practice but it is a tedious and error-prone process, which does not scale well. Solutions for specific parts of the problem have been proposed but a complete reassembly and repair pipeline is absent from the bibliography. We propose a shape restoration pipeline consisting of appropriate methods for automatic fragment reassembly and shape completion. We demonstrate the effectiveness of our approach using real-world fractured objects. We suggest two main restoration phases. In the first phase, fragments are reassembled. The reassembly solution results from finding the Minimum Spanning Forest for pairwise matches between fragment contact surfaces, which have been identified in a preprocessing step . The reassembly is guided by global error relaxation and can also make use of external feature curves on the fragments. The obtained reassembly solution typically misses some parts of the shape, due to missing or eroded fragments. Therefore, in the second phase we compute plausible complete versions of the reassembles partial shapes. This is done by robust detection of global shape symmetries which relies on local shape features. Completion of non-symmetric shapes is assisted by template repair shapes retrieved by a partial 3D similarity search. The final shape is finished by merging and smoothing of the obtained parts, inpainting of missing local shape information, and export of synthesized missing parts for physical restoration.
This work introduces a partial 3D object retrieval method, applicable on both meshes and point clouds, which is based on a hybrid shape matching scheme combining local shape descriptors with global Fisher vectors. The differential fast point feature histogram (dFPFH) is defined so as to extend the well-known FPFH descriptor in order to capture local geometry transitions. Local shape similarity is quantified by averaging the minimum weighted distances associated with pairs of dFPFH values calculated on the partial query and the target object. Global shape similarity is derived by means of a weighted distance of Fisher vectors. Local and global distances are derived for multiple scales and are being combined to obtain a ranked list of the most similar complete 3D objects. Experiments on the large-scale benchmark dataset for partial object retrieval of the shape retrieval contest (SHREC) 2013, as well as on the publicly available Hampson pottery dataset, support improved performance of the proposed method against seven recently evaluated retrieval methods.
Recently, 3D digitization and printing hardware have seen rapidly increasing adoption. High-quality digitization of real-world objects is becoming more and more efficient. In this context, growing amounts of data from the cultural heritage (CH) domain such as columns, tombstones or arches are being digitized and archived in 3D repositories. In many cases, these objects are not complete, but fragmented into several pieces and eroded over time. As manual restoration of fragmented objects is a tedious and error-prone process, recent work has addressed automatic reassembly and completion of fragmented 3D data sets. While a growing number of related techniques are being proposed by researchers, their evaluation currently is limited to smaller numbers of high-quality test fragment sets.We address this gap by contributing a methodology to automatically generate 3D fragment data based on synthetic fracturing of 3D input objects. Our methodology allows generating large-scale fragment test data sets from existing CH object models, complementing manual benchmark generation based on scanning of fragmented real objects. Besides being scalable, our approach also has the advantage to come with ground truth information (i.e. the input objects), which is often not available when scans of real fragments are used. We apply our approach to the Hampson collection of digitized pottery objects, creating and making available a first, larger restoration test data set that comes with ground truth. Furthermore, we illustrate the usefulness of our test data for evaluation of a recent 3D restoration method based on symmetry analysis and also outline how the applicability of 3D retrieval techniques could be evaluated with respect to 3D restoration tasks. Finally, we discuss first results of an ongoing extension of our methodology to include object erosion processes by means of a physiochemical model simulating weathering effects.
The registration of two geometric surfaces is typically addressed using variants of the Iterative Closest Point (ICP) algorithm. The Sparse ICP method formulates the problem using sparsity-inducing norms, significantly improving the resilience of the registration process to large amounts of noise and outliers, but introduces a significant performance degradation. In this paper we first identify the reasons for this performance degradation and propose a hybrid optimization system that combines a Simulated Annealing search along with the standard Sparse ICP, in order to solve the underlying optimization problem more efficiently. We also provide several insights on how to further improve the overall efficiency by using a combination of approximate distance queries, parallel execution and uniform subsampling. The resulting method provides cumulative performance gain of more than one order of magnitude, as demonstrated through the registration of partially overlapping scans with various degrees of noise and outliers.
This work offers an overview of the state-of-the-art on the emerging area of 3D object retrieval based on partial queries. This research area is associated with several application domains, including face recognition and digital libraries of cultural heritage objects. The existing partial 3D object retrieval methods can be mainly classified as: i) view-based, ii) partbased, iii) bag of visual words (BoVW)-based, and iv) hybrid methods combining these three main paradigms or methods which cannot be straightforwardly classified. Several methodological aspects are identified, including the use of interest points and the exploitation of 2.5D projections, whereas the available evaluation datasets and campaigns are addressed. A thorough discussion follows, identifying advantages and limitations.
In this paper, we present a method for partial matching and retrieval of 3D objects based on range image queries. The proposed methodology addresses the retrieval of complete 3D objects using range image queries that represent partial views. The core methodology relies upon Bag-of-Visual-Words modelling and enhanced Dense SIFT descriptor computed on panoramic views and range image queries. Performance evaluation builds upon standard measures and a challenging 3D pottery dataset originating from the Hampson Archaeological Museum collection.