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.
The reassembly of fractured 3D objects from their parts is an important problem in cultural heritage and otherdomains.We approach reassembly from a geometric matching perspective and propose a pipeline for the automatic solution of the problem, where an efficient and generic three-level coarse-to-fine search strategy is used for the underlying global optimization. Key to the efficiency of our approach is the use of a discretized approximation of the surfaces’ distance field, which significantly reduces the cost of distance queries and allows our method to systematically search the global parameter space with minimal cost. The resulting reassembly pipeline provides highly reliable alignment, as demonstrated through the reassembly of fractured objects from their fragments and the reconstruction of 3D objects from partial scans, showcasing the wide applicability of our methodology.
In this paper we present a novel generic method for the fast and accurate computation of geometric features at multiple scales. The presented method works on arbitrarily complex models and operates in the parametric space. The majority of the existing methods compute local features directly on the geometric representation of the model. Our approach decouples the computational complexity from the underlying geometry and in contrast to other parametric space methods, it is not restricted to a specific feature or parameterization of the surface. We show that the method performs accurately and at interactive rates, even for large feature areas of support, rendering the method suitable for animated shapes.
In order to decide which preservation strategies one should follow for Cultural Heritage objects made of stone, it is necessary to estimate the impact of different alternative strategies. A useful aid in such a process is the simulation of the stone degradation process. The first step in such a process is to create realistic digital representations of stones. In this work we present early efforts to create 3D voxelized representations of stones from given 2D example-textures of the desired stone-material. The process aims at the creation of 3D stone materials, consistentwith 2D samples. The presented work is one part of the PRESIOUS EU project.
Due to recent improvements in 3D acquisition and shape processing technology, the digitization of Cultural Heritage (CH) artifacts is gaining increased application in context of archival and archaeological research. This increasing availability of acquisition technologies also implies a need for intelligent processing methods that can cope with imperfect object scans. Specifically for Cultural Heritage objects, besides imperfections given by the digitization process, also the original artifact objects may be imperfect due to deterioration or fragmentation processes. Currently, the reconstruction of previously digitized CH artifacts is mostly performed manually by expert users reassembling fragment parts and completing imperfect objects by modeling. However, more automatic methods for CH object repair and completion are needed to cope with increasingly large data becoming available. In this conceptual paper, we first provide a brief survey of typical imperfections in CH artifact scan data and in turn motivate the need for respective repair methods. We survey and classify a selection of existing reconstruction methods with respect to their applicability for CH objects, and then discuss how these approaches can be extended and combined to address various types of physical defects that are encountered in CH artifacts by proposing a flexible repair workflow for 3D digitizations of CH objects. The workflow accommodates an automatic reassembly step which can deal with fragmented input data. It also includes the similarity-based retrieval of appropriate complementary object data which is used to repair local and global object defects. Finally, we discuss options for evaluation of the effectiveness of such a CH repair workflow.
Symmetry is a common characteristic in natural and man-made objects. Its ubiquitous nature can be exploited to facilitate the analysis and processing of computational representations of real objects. In particular, in computer graphics, the detection of symmetries in 3D geometry has enabled a number of applications in modeling and reconstruction. However, the problem of symmetry detection in incomplete geometry remains a challenging task. In this paper, we propose a vote-based approach to detect symmetry in 3D shapes, with special interest in modelswith large missing parts. Our algorithm generates a set of candidate symmetries by matching local maxima of a surface function based on the heat diffusion in local domains, which guarantee robustness to missing data. In order to deal with local perturbations, we propose a multi-scale surface function that is useful to select a set of distinctive points over which the approximate symmetries are defined. In addition, we introduce a vote-based scheme that is aware of the partiality, and therefore reduces the number of false positive votes for the candidate symmetries. We show the effectiveness of our method in a varied set of 3D shapes and different levels of partiality. Furthermore, we show the applicability of our algorithm in the repair and completion of challenging reassembled objects in the context of cultural heritage.
In this paper, we address the evaluation of algorithms for partial shape retrieval using a large-scale simulated benchmark of partial views which are used as queries. Since the scanning of real objects is a time-consuming task, we create a simulation that generates a set of views from a target model and at different levels of complexity (amount of missing data). In total, our benchmark contains 7,200 partial views. Furthermore, we propose the use of weighted effectiveness measures based on the complexity of a query. With these characteristics, we aim at jointly evaluating the effectiveness, efficiency and robustness of existing algorithms. As a result of our evaluation, we found that a combination of methods provides the best effectiveness, mainly due to the complementary information that they deliver. The obtained results open new questions regarding the diculty of the partial shape retrieval problem. As aconsequence, potential future directions are also identified.
The reassembly of fractured 3D objects is a critical problem in computational archaeology, and other application domains. An essential part of this problem is to distinguish the regions of the object that belong to the original surface from the fractured ones. A general strategy to solve this region classification problem is to first divide the surface of the object into distinct facets and then classify each one of them based on statistical properties. While many relevant algorithms have been previously proposed, a comparative evaluation of some well-known segmentation strategies, when used in the context of such a problem, is absent from the bibliography. In this poster we present our ongoing work on the evaluation of the performance and quality of segmentation algorithms when operating on fractured objects. We also present a novel method for the classification of the segmented regions to intact and fractured, based on their statistical properties.
Cultural heritage is a natural application domain for partial 3D object retrieval, since it usually involves objects that have only been partially preserved. This work introduces a method for the retrieval of 3D pottery objects, based on partial point cloud queries. The proposed method extracts fast persistent feature histograms calculated adaptively to the mean point distances of the point cloud query. The extracted set of vectors is refined by a denoising component, which employs statistical filtering. The remaining vectors are further refined by a filtering component, which discards points surrounded by surfaces of extremely fine-grained irregularity, often associated with artefact damages. A bag of visual words scheme is used, which starts from the final set of persistent feature histogram vectors and estimates Gaussian mixture models by means of an expectation maximization algorithm. The resulting Gaussian mixture models define the visual codebook, which is used within the context of Fisher encoding. Experiments are performed on a challenging dataset of pottery objects, obtained from the publicly available Hampson collection.