3DOR 16

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3D Object Retrieval with Multimodal Views
(The Eurographics Association, 2016) Gao, Yue; Nie, Weizhi; Liu, Anan; Su, Yuting; Dai, Qionghai; An, Le; Chen, Fuhai; Cao, Liujuan; Dong, Shuilong; De, Yu; Gao, Zan; Hao, Jiayun; Ji, Rongrong; Li, Haisheng; Liu, Mingxia; Pan, Lili; Qiu, Yu; Wei, Liwei; Wang, Zhao; Wei, Hongjiang; Zhang, Yuyao; Zhang, Jun; Zhang, Yang; Zheng, Yali; A. Ferreira and A. Giachetti and D. Giorgi
This paper reports the results of the SHREC'16 track: 3D Object Retrieval with Multimodal Views, whose goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 605 objects is generated and both the color images and the depth images are provided for each object. 200 objects including 100 3D printing models and 100 3D real objects are selected as the queries while the other 405 objects are selected as the tests and average retrieval performance is measured. The track attracted seven participants and the submission of 9 runs. Comparing to the last year's results, 3D printing models obviously introduce a bad influence. The performance of this year is worse than that of last year. This condition also shows a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data.
3D Objects Exploration: Guidelines for Future Research
(The Eurographics Association, 2016) Biasotti, Silvia; Falcidieno, Bianca; Giorgi, Daniela; Spagnuolo, Michela; A. Ferreira and A. Giachetti and D. Giorgi
Search engines provide the interface to interact with 3D object repositories, which are rapidly growing in both number and size. This position paper presents the current state of the art on 3D dataset navigation and 3D model retrieval. We discuss a number of challenges we consider as the main points to be tackled for developing effective 3D object exploration systems.
3D Sketch-Based 3D Shape Retrieval
(The Eurographics Association, 2016) Li, Bo; Lu, Yijuan; Duan, Fuqing; Dong, Shuilong; Fan, Yachun; Qian, Lu; Laga, Hamid; Li, Haisheng; Li, Yuxiang; Liu, Peng; Ovsjanikov, Maks; Tabia, Hedi; Ye, Yuxiang; Yin, Huanpu; Xue, Ziyu; A. Ferreira and A. Giachetti and D. Giorgi
Sketch-based 3D shape retrieval has unique representation availability of the queries and vast applications. Therefore, it has received more and more attentions in the research community of content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate representation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the research on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketching interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D sketch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D model target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups and their retrieval performance has been evaluated using seven commonly used retrieval performance metrics.We wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote sketch-based 3D shape retrieval and its applications.
3DOR 2016: Frontmatter
(Eurographics Association, 2016) Alfredo Ferreira; Andrea Giachetti; Daniela Giorgi;
A Descriptor for Voxel Shapes Based on the Skeleton Cut Space
(The Eurographics Association, 2016) Feng, Cong; Jalba, Andrei C.; Telea, Alexandru C.; A. Ferreira and A. Giachetti and D. Giorgi
Two-dimensional medial axes and three-dimensional curve skeletons have been long used for shape retrieval tasks. In contrast, and despite their ability to fully capture shape geometry and topology, three-dimensional surface skeletons have seen much less usage in this context. We present here a framework for shape matching and retrieval based on such surface skeletons. To this end, we construct a space of cuts generated by the surface skeleton, which has desirable invariance properties with respect to shape size, rotation, translation, pose, and noise. Next, we extract a histogram-based descriptor from this cut space, and discuss three different metrics to compare such histograms for shape retrieval. We illustrate our proposal by showing our descriptor's effectiveness in shape retrieval using a known shape-database benchmark.
An Edit Distance for Reeb Graphs
(The Eurographics Association, 2016) Bauer, Ulrich; Fabio, Barbara Di; Landi, Claudia; A. Ferreira and A. Giachetti and D. Giorgi
We consider the problem of assessing the similarity of 3D shapes using Reeb graphs from the standpoint of robustness under perturbations. For this purpose, 3D objects are viewed as spaces endowed with real-valued functions, while the similarity between the resulting Reeb graphs is addressed through a graph edit distance. The cases of smooth functions on manifolds and piecewise linear functions on polyhedra stand out as the most interesting ones. The main contribution of this paper is the introduction of a general edit distance suitable for comparing Reeb graphs in these settings. This edit distance promises to be useful for applications in 3D object retrieval because of its stability properties in the presence of noise.
An Evaluation of Local Feature Encodings for Shape Retrieval
(The Eurographics Association, 2016) Tasse, Flora Ponjou; Kosinka, Jiri; Dodgson, Neil A.; A. Ferreira and A. Giachetti and D. Giorgi
Local features are successfully used in 3D shape retrieval by encoding features descriptors into global shape signatures. Previous 3D retrieval systems use different encoding methods, such as histogram encoding and Fisher encodings, making it difficult to evaluate one encoding technique against another. We perform a comparative analysis of four recent encoding methods when used in shape retrieval. The analysis shows that Vector of Locally Aggregated Descriptors (VLAD) encoding is the best method of the four tested, since it offers the best trade-off between precision and computational cost.
An Experimental Shape Matching Approach for Protein Docking
(The Eurographics Association, 2016) Fernandes, Francisco; Ferreira, Alfredo; A. Ferreira and A. Giachetti and D. Giorgi
Proteins play a vital role in biological processes, with their function being largely determined by their structure. It is important to know what a protein binds, where it binds, how it binds, and what is its final conformation. Several methodologies have been applied to solve this complex protein-protein docking problem, but the number of degrees of freedom renders this a very slow and computationally heavy challenge. To handle this problem, we propose a multi-level space partition approach to describe the three-dimensional shape of the protein. By combining two proteins in the same data structure we are able to easily detect the shape-complementary regions. Moreover, by directly integrating bio-energetic information, we can drive the algorithm by both parameters and provide a fast and efficient way to overcome some of the limitations of previous approaches.
Large-Scale 3D Shape Retrieval from ShapeNet Core55
(The Eurographics Association, 2016) Savva, M.; Yu, F.; Su, Hao; Aono, M.; Chen, B.; Cohen-Or, D.; Deng, W.; Su, Hang; Bai, S.; Bai, X.; Fish, N.; Han, J.; Kalogerakis, E.; Learned-Miller, E. G.; Li, Y.; Liao, M.; Maji, S.; Tatsuma, A.; Wang, Y.; Zhang, N.; Zhou, Z.; A. Ferreira and A. Giachetti and D. Giorgi
With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track aims to provide a benchmark to evaluate large-scale shape retrieval based on the ShapeNet dataset. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Five participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. We find the submitted methods work reasonably well on the track benchmark, but we also see significant space for improvement by future algorithms. We release all the data, results, and evaluation code for the benefit of the community.
Matching of Deformable Shapes with Topological Noise
(The Eurographics Association, 2016) Lähner, Z.; Rodolà, E.; Bronstein, M. M.; Cremers, D.; Burghard, O.; Cosmo, L.; Dieckmann, A.; Klein, R.; Sahillioglu, Y.; A. Ferreira and A. Giachetti and D. Giorgi
A particularly challenging setting of the shape matching problem arises when the shapes being matched have topological artifacts due to the coalescence of spatially close surface regions - a scenario that frequently occurs when dealing with real data under suboptimal acquisition conditions. This track of the SHREC'16 contest evaluates shape matching algorithms that operate on 3D shapes under synthetically produced topological changes. The task is to produce a pointwise matching (either sparse or dense) between 90 pairs of shapes, representing the same individual in different poses but with different topology. A separate set of 15 shapes with ground-truth correspondence was provided as training data for learning-based techniques and for parameter tuning. Three research groups participated in the contest; this paper presents the track dataset, and describes the different methods and the contest results.
Partial Matching of Deformable Shapes
(The Eurographics Association, 2016) Cosmo, L.; Rodolà, E.; Bronstein, M. M.; Torsello, A.; Cremers, D.; Sahillioglu, Y.; A. Ferreira and A. Giachetti and D. Giorgi
Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method - making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods.
Partial Shape Queries for 3D Object Retrieval
(The Eurographics Association, 2016) Pratikakis, I.; Savelonas, M.A.; Arnaoutoglou, F.; Ioannakis, G.; Koutsoudis, A.; Theoharis, T.; Tran, M.-T.; Nguyen, V.-T.; Pham, V.-K.; Nguyen, H.-D.; Le, H.-A.; Tran, B.-H.; To, Q.H.; Truong, M.-B.; Phan, T.V.; Nguyen, M.-D.; Than, T.-A.; Mac, K.-N.C.; Do, M.N.; Duong, A-D.; Furuya, T.; Ohbuchi, R.; Aono, M.; Tashiro, S.; Pickup, D.; Sun, X.; Rosin, P.L.; Martin, R.R.; A. Ferreira and A. Giachetti and D. Giorgi
Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes.
Retrieval of Human Subjects from Depth Sensor Data
(The Eurographics Association, 2016) Giachetti, A.; Fornasa, F.; Parezzan, F.; Saletti, A.; Zambaldo, L.; Zanini, L.; Achilles, F.; Ichim, A. E.; Tombari, F.; Navab, N.; Velasco-Forero, S.; A. Ferreira and A. Giachetti and D. Giorgi
In this paper we report the results of the SHREC 2016 contest on "Retrieval of human subjects from depth sensor data". The proposed task was created in order to verify the possibility of retrieving models of query human subjects from single shots of depth sensors, using shape information only. Depth acquisition of different subjects were realized under different illumination conditions, using different clothes and in three different poses. The resulting point clouds of the partial body shape acquisitions were segmented and coupled with the skeleton provided by the OpenNI software and provided to the participants together with derived triangulated meshes. No color information was provided. Retrieval scores of the different methods proposed were estimated on the submitted dissimilarity matrices and the influence of the different acquisition conditions on the algorithms were also analyzed. Results obtained by the participants and by the baseline methods demonstrated that the proposed task is, as expected, quite difficult, especially due the partiality of the shape information and the poor accuracy of the estimated skeleton, but give useful insights on potential strategies that can be applied in similar retrieval procedures and derived practical applications.
Shape Retrieval and 3D Gestural Interaction
(The Eurographics Association, 2016) Giachetti, Andrea; Caputo, Fabio Marco; Carcangiu, Alessandro; Scateni, Riccardo; Spano, Lucio Davide; A. Ferreira and A. Giachetti and D. Giorgi
Despite the emerging importance of Virtual Reality and immersive interaction research, no papers on application of 3D shape retrieval to this topic have been presented in recent 3D Object Retrieval workshops. In this paper we discuss how geometric processing and geometric shape retrieval methods could be extremely useful to implement effective natural interaction systems for 3D immersive virtual environments. In particular, we will discuss how the reduction of complex gesture recognition tasks to simple geometric retrieval ones could be useful to solve open issue in gestural interaction. Algorithms for robust point description in trajectories data with learning of inter-subject invariant features could, for example, solve relevant issues of direct manipulation algorithms, and 3D object retrieval methods could be used as well to build dictionaries and implement guidance system to maximize usability of natural gestural interfaces.
Shape Retrieval of Low-Cost RGB-D Captures
(The Eurographics Association, 2016) Pascoal, Pedro B.; Proença, Pedro; Gaspar, Filipe; Dias, Miguel Sales; Ferreira, Alfredo; Tatsuma, Atsushi; Aono, Masaki; Logoglu, K. Berker; Kalkan, Sinan; Temizel, Alptekin; Li, Bo; Johan, Henry; Lu, Yijuan; Seib, Viktor; Link, Norman; Paulus, Dietrich; A. Ferreira and A. Giachetti and D. Giorgi
RGB-D cameras allow to capture digital representations of objects in an easy and inexpensive way. Such technology enables ordinary users to capture everyday object into digital 3D representations. In this context, we present a track for the Shape Retrieval Contest, which focus on objects digitized using the latest version of Microsoft Kinect, namely, Kinect One. The proposed, track encompasses a dataset of two hundred objects and respective classification.
Towards an Observer-oriented Theory of Shape Comparison
(The Eurographics Association, 2016) Frosini, Patrizio; A. Ferreira and A. Giachetti and D. Giorgi
In this position paper we suggest a possible metric approach to shape comparison that is based on a mathematical formalization of the concept of observer, seen as a collection of suitable operators acting on a metric space of functions. These functions represent the set of data that are accessible to the observer, while the operators describe the way the observer elaborates the data and enclose the invariance that he/she associates with them. We expose this model and illustrate some theoretical reasons that justify its possible use for shape comparison.

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