Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.Comment: 19 pages, 17 figures, to be published in Computers & Graphic

A Fast Modal Space Transform for Robust Nonrigid Shape Retrieval

Nonrigid or deformable 3D objects are common in many application domains. Retrieval of such objects in large databases based on shape similarity is still a challenging problem. In this paper, we take advantages of functional operators as characterizations of shape deformation, and further propose a framework to design novel shape signatures for encoding nonrigid geometries. Our approach constructs a context-aware integral kernel operator on a manifold, then applies modal analysis to map this operator into a low-frequency functional representation, called fast functional transform, and finally computes its spectrum as the shape signature. In a nutshell, our method is fast, isometry-invariant, discriminative, smooth and numerically stable with respect to multiple types of perturbations. Experimental results demonstrate that our new shape signature for nonrigid objects can outperform all methods participating in the nonrigid track of the SHREC’11 contest. It is also the second best performing method in the real human model track of SHREC’14.postprin

Combination of bag-of-words descriptors for robust partial shape retrieval

International audienceThis paper presents a 3D shape retrieval algorithmbased on the Bag of Words (BoW) paradigm.For a given 3D shape, the proposed approach considersa set of feature points uniformly sampled on the surfaceand associated with local Fourier descriptors; thisdescriptor is computed in the neighborhood of each featurepoint by projecting the geometry onto the eigenvectorsof the Laplace-Beltrami operator, it is very informative,robust to connectivity and geometry changesand also fast to compute. In a preliminary step, a visualdictionary is built by clustering a large set of featuredescriptors, then each 3D shape is described by anhistogram of occurrences of these visual words, hencediscarding any spatial information. A spatially-sensitivealgorithm is also presented where the 3D shape is describedby an histogram of pairs of visual words. Weshow that these two approaches are complementary andcan be combined to improve the performance and therobustness of the retrieval. The performances have beencompared against very recent state-of-the-art methodson several different datasets. For global shape retrievalour combined approach is comparable to these recentworks, however it clearly outperforms them in the caseof partial shape retrieval

Combination of Bag-of-Words Descriptors for Robust Partial Shape Retrieval

International audienceThis paper presents a 3D shape retrieval algorithmbased on the Bag of Words (BoW) paradigm.For a given 3D shape, the proposed approach considersa set of feature points uniformly sampled on the surfaceand associated with local Fourier descriptors; thisdescriptor is computed in the neighborhood of each featurepoint by projecting the geometry onto the eigenvectorsof the Laplace-Beltrami operator, it is very informative,robust to connectivity and geometry changesand also fast to compute. In a preliminary step, a visualdictionary is built by clustering a large set of featuredescriptors, then each 3D shape is described by anhistogram of occurrences of these visual words, hencediscarding any spatial information. A spatially-sensitivealgorithm is also presented where the 3D shape is describedby an histogram of pairs of visual words. Weshow that these two approaches are complementary andcan be combined to improve the performance and therobustness of the retrieval. The performances have beencompared against very recent state-of-the-art methodson several different datasets. For global shape retrievalour combined approach is comparable to these recentworks, however it clearly outperforms them in the caseof partial shape retrieval

Combination of Bag-of-Words Descriptors for Robust Partial Shape Retrieval

International audienceThis paper presents a 3D shape retrieval algorithmbased on the Bag of Words (BoW) paradigm.For a given 3D shape, the proposed approach considersa set of feature points uniformly sampled on the surfaceand associated with local Fourier descriptors; thisdescriptor is computed in the neighborhood of each featurepoint by projecting the geometry onto the eigenvectorsof the Laplace-Beltrami operator, it is very informative,robust to connectivity and geometry changesand also fast to compute. In a preliminary step, a visualdictionary is built by clustering a large set of featuredescriptors, then each 3D shape is described by anhistogram of occurrences of these visual words, hencediscarding any spatial information. A spatially-sensitivealgorithm is also presented where the 3D shape is describedby an histogram of pairs of visual words. Weshow that these two approaches are complementary andcan be combined to improve the performance and therobustness of the retrieval. The performances have beencompared against very recent state-of-the-art methodson several different datasets. For global shape retrievalour combined approach is comparable to these recentworks, however it clearly outperforms them in the caseof partial shape retrieval

Efficient Retrieval and Categorization for 3D Models based on Bag-of-Words Approach

Ph.DDOCTOR OF PHILOSOPH

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

3D object retrieval and segmentation: various approaches including 2D poisson histograms and 3D electrical charge distributions.

Nowadays 3D models play an important role in many applications: viz. games, cultural heritage, medical imaging etc. Due to the fast growth in the number of available 3D models, understanding, searching and retrieving such models have become interesting fields within computer vision. In order to search and retrieve 3D models, we present two different approaches: one is based on solving the Poisson Equation over 2D silhouettes of the models. This method uses 60 different silhouettes, which are automatically extracted from different viewangles. Solving the Poisson equation for each silhouette assigns a number to each pixel as its signature. Accumulating these signatures generates a final histogram-based descriptor for each silhouette, which we call a SilPH (Silhouette Poisson Histogram). For the second approach, we propose two new robust shape descriptors based on the distribution of charge density on the surface of a 3D model. The Finite Element Method is used to calculate the charge density on each triangular face of each model as a local feature. Then we utilize the Bag-of-Features and concentric sphere frameworks to perform global matching using these local features. In addition to examining the retrieval accuracy of the descriptors in comparison to the state-of-the-art approaches, the retrieval speeds as well as robustness to noise and deformation on different datasets are investigated. On the other hand, to understand new complex models, we have also utilized distribution of electrical charge for proposing a system to decompose models into meaningful parts. Our robust, efficient and fully-automatic segmentation approach is able to specify the segments attached to the main part of a model as well as locating the boundary parts of the segments. The segmentation ability of the proposed system is examined on the standard datasets and its timing and accuracy are compared with the existing state-of-the-art approaches