Konstantinos Sfikas

Non-rigid 3D object retrieval using topological information guided by conformal factors

Combining the properties of conformal geometry and graph-based topological information, a non-rigid 3D object retrieval methodology is proposed, which is both robust and efficient in terms of retrieval accuracy and computation speed. While graph-based methods are robust to non-rigid object deformations, they require intensive computation which can be reduced by the use of appropriate representations, addressed through geometry-based methods. In this respect, we present a 3D object retrieval methodology, which combines the above advantages in a unified manner. Furthermore, we propose a string matching strategy for the comparison of graphs which describe 3D objects.

An overview of partial 3D object retrieval methodologies

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) part-based, 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.

ROSy+: 3D Object Pose Normalization Based on PCA and Reflective Object Symmetry with Application in 3D Object Retrieval

A novel pose normalization method based on 3D object reflective symmetry is presented. It is a general purpose global pose normalization method; in this paper it is used to enhance the performance of a 3D object retrieval pipeline. Initially, the axis-aligned minimum bounding box of a rigid 3D object is modified by requiring that the 3D object is also in minimum angular difference with respect to the normals to the faces of its bounding box. To estimate the modified axis-aligned bounding box, a set of predefined planes of symmetry are used and a combined spatial and angular distance, between the 3D object and its symmetric object, is calculated. By minimizing the combined distance, the 3D object fits inside its modified axis-aligned bounding box and alignment with the coordinate system is achieved. The proposed method is incorporated in a hybrid scheme, that serves as the alignment method in a 3D object retrieval system. The effectiveness of the 3D object retrieval system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and demonstrated using both quantitative and qualitative measures via an extensive consistent evaluation on standard benchmarks. The results clearly show performance boost against current approaches.

Partial matching of 3D cultural heritage objects using panoramic views

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.

Partial 3D object retrieval combining local shape descriptors with global fisher vectors

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.

ConTopo: non-rigid 3D object retrieval using topological information guided by conformal factors

Combining the properties of conformal geometry and graph-based topological information for 3D object retrieval, a non-rigid 3D object descriptor is proposed, which is both robust and efficient in terms of retrieval accuracy and computation speed. In previous works, graph-based methods for non-rigid 3D object retrieval, have shown high discriminative power and robustness, while geometry-based methods, have proven to be tolerant to noise and pose. In this work, we present a 3D object descriptor that combines the above advantages.

Fisher encoding of differential fast point feature histograms for partial 3D object retrieval

Partial 3D object retrieval has attracted intense research efforts due to its potential for a wide range of applications, such as 3D object repair and predictive digitization. This work introduces a partial 3D object retrieval method, applicable on both point clouds and structured 3D models, which is based on a shape matching scheme combining local shape descriptors with their Fisher encodings. Experiments on the SHREC 2013 large-scale benchmark dataset for partial object retrieval, as well as on the publicly available Hampson pottery dataset, demonstrate that the proposed method outperforms seven recently evaluated partial retrieval methods. HighlightsFirst application of Fisher encoding in 3D object retrieval.The dFPFH extends FPFH, capturing more accurately local geometric transitions.The proposed similarity addresses partiality.The proposed method outperforms state-of-the-art in SHREC 2013 dataset.The proposed method outperforms state-of-the-art in cultural heritage datasets.

Fisher encoding of adaptive fast persistent feature histograms for partial retrieval of 3D pottery objects

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 de-noising 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.

3D object retrieval via range image queries in a bag-of-visual-words context

Abstract3D object retrieval based on range image queries that represent partial views of real 3D objects is presented. The complete 3D models of the database are described by a set of panoramic views, and a Bag-of-Visual-Words model is built using SIFT features extracted from them. To address the problem of partial matching, we suggest a histogram computation scheme, on the panoramic views, that represents local information by taking into account spatial context. Furthermore, a number of optimization techniques are applied throughout the process for enhancing the retrieval performance. Its superior performance is shown by evaluating it against state-of-the-art methods on standard datasets.

3D Object Partial Matching Using Panoramic Views

In this paper, a methodology for 3D object partial matching and retrieval based on range image queries is presented. The proposed methodology addresses the retrieval of complete 3D objects based on artificially created range image queries which represent partial views. The core methodology relies upon Dense SIFT descriptors computed on panoramic views. Performance evaluation builds upon the standard measures and a challenging 3D pottery dataset originated from the Hampson Archeological Museum collection.