3DOR: Eurographics Workshop on 3D Object Retrieval

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2D Image-Based 3D Scene Retrieval
(The Eurographics Association, 2018) Abdul-Rashid, Hameed; Yuan, Juefei; Li, Bo; Lu, Yijuan; Bai, Song; Bai, Xiang; Bui, Ngoc-Minh; Do, Minh N.; Do, Trong-Le; Duong, Anh-Duc; He, Xinwei; Le, Tu-Khiem; Li, Wenhui; Liu, Anan; Liu, Xiaolong; Nguyen, Khac-Tuan; Nguyen, Vinh-Tiep; Nie, Weizhi; Ninh, Van-Tu; Su, Yuting; Ton-That, Vinh; Tran, Minh-Triet; Xiang, Shu; Zhou, Heyu; Zhou, Yang; Zhou, Zhichao; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retrieval. Given a 2D scene image, it is to search for relevant 3D scenes from a dataset. It has an intuitive and convenient framework which allows users to learn, search, and utilize the retrieved results for vast related applications, such as automatic 3D content generation for 3D movie, game and animation production, robotic vision, and consumer electronics apps development, and autonomous vehicles. To advance this promising research, we organize this SHREC track and build the first 2D scene image-based 3D scene retrieval benchmark by collecting 2D images from ImageNet and 3D scenes from Google 3D Warehouse. The benchmark contains uniformly classified 10,000 2D scene images and 1,000 3D scene models of ten (10) categories. In this track, seven (7) groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only three (3) groups have successfully submitted ten (10) runs of five methods. To have a comprehensive comparison, seven (7) commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. We also suggest several future research directions for this research topic. We wish this publicly available [ARYLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and boost the research of 2D scene image-based 3D scene retrieval and its applications.
2D Scene Sketch-Based 3D Scene Retrieval
(The Eurographics Association, 2018) Yuan, Juefei; Li, Bo; Lu, Yijuan; Bai, Song; Bai, Xiang; Bui, Ngoc-Minh; Do, Minh N.; Do, Trong-Le; Duong, Anh-Duc; He, Xinwei; Le, Tu-Khiem; Li, Wenhui; Liu, Anan; Liu, Xiaolong; Nguyen, Khac-Tuan; Nguyen, Vinh-Tiep; Nie, Weizhi; Ninh, Van-Tu; Su, Yuting; Ton-That, Vinh; Tran, Minh-Triet; Xiang, Shu; Zhou, Heyu; Zhou, Yang; Zhou, Zhichao; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the field of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location configurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration. To promote this interesting research, we organize this SHREC track and build the first 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classified into 10 classes. In this track, six groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those methods, and suggest several future research directions to tackle this research problem. We wish this publicly available [YLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and advance the research of 2D scene sketch-based 3D scene retrieval and its applications.
A 3D CAD Assembly Benchmark
(The Eurographics Association, 2019) Lupinetti, Katia; Giannini, Franca; monti, marina; PERNOT, Jean-Philippe; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
Evaluating the effectiveness of the systems for the retrieval of 3D assembly models is not trivial. CAD assembly models can be considered similar according to different criteria and at different levels (i.e. globally or partially). Indeed, besides the shape criterion, CAD assembly models have further characteristic elements, such as the mutual position of parts, or the type of connecting joint. Thus, when retrieving 3D models, these characteristics can match in the entire model (globally) or just in local subparts (partially). The available 3D model repositories do not include complex CAD assembly models and, generally, they are suitable to evaluate one characteristic at a time and neglecting important properties in the evaluation of assembly similarity. In this paper, we present a benchmark for the evaluation of content-retrieval systems of 3D assembly models. A crucial feature of this benchmark regards its ability to consider the various aspects characterizing the models of mechanical assemblies.
A 3D Face Recognition Algorithm Using Histogram-based Features
(The Eurographics Association, 2008) Zhou, Xuebing; Seibert, Helmut; Busch, Christoph; Funk, Wolfgang; Stavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmann
We present an automatic face recognition approach, which relies on the analysis of the three-dimensional facial surface. The proposed approach consists of two basic steps, namely a precise fully automatic normalization stage followed by a histogram-based feature extraction algorithm. During normalization the tip and the root of the nose are detected and the symmetry axis of the face is determined using a PCA analysis and curvature calculations. Subsequently, the face is realigned in a coordinate system derived from the nose tip and the symmetry axis, resulting in a normalized 3D model. The actual region of the face to be analyzed is determined using a simple statistical method. This area is split into disjoint horizontal subareas and the distribution of depth values in each subarea is exploited to characterize the face surface of an individual. Our analysis of the depth value distribution is based on a straightforward histogram analysis of each subarea. When comparing the feature vectors resulting from the histogram analysis we apply three different similarity metrics. The proposed algorithm has been tested with the FRGC v2 database, which consists of 4950 range images. Our results indicate that the city block metric provides the best classification results with our feature vectors. The recognition system achieved an equal error rate of 5.89% with correctly normalized face models.
3D GrabCut: Interactive Foreground Extraction for Reconstructed 3D Scenes
(The Eurographics Association, 2015) Meyer, Gregory P.; Do, Minh N.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
In the near future, mobile devices will be able to measure the 3D geometry of an environment using integrated depth sensing technology. This technology will enable anyone to reconstruct a 3D model of their surroundings. Similar to natural 2D images, a 3D model of a natural scene will occasionally contain a desired foreground object and an unwanted background region. Inspired by GrabCut for still images, we propose a system to perform interactive foreground/background segmentation on a reconstructed 3D scene using an intuitive user interface. Our system is designed to enable anyone, regardless of skill, to extract a 3D object from a 3D scene with a minimal amount of effort. The only input required by the user is a rectangular box around the desired object. We performed several experiments to demonstrate that our system produces high-quality segmentation on a wide variety of 3D scenes.
3D Hand Gesture Recognition Using a Depth and Skeletal Dataset
(The Eurographics Association, 2017) Smedt, Quentin De; Wannous, Hazem; Vandeborre, Jean-Philippe; Guerry, J.; Saux, B. Le; Filliat, D.; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging hand gesture dataset containing 14 gestures, performed by 28 participants executing the same gesture with two different numbers of fingers. Two research groups have participated to this track, the accuracy of their recognition algorithms have been evaluated and compared to three other state-of-the-art approaches.
3D Human Video Retrieval: from Pose to Motion Matching
(The Eurographics Association, 2013) Slama, Rim; Wannous, Hazem; Daoudi, Mohamed; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
3D video retrieval is a challenging problem lying at the heart of many primary research areas in computer graphics and computer vision applications. In this paper, we present a new 3D human shape matching and motion retrieval framework. Our approach is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface and a local motion retrieval achieved after motion segmentation. Matching is performed by an efficient method which takes advantage of a compact EHC representation in open curve Shape Space and an elastic distance measure. Moreover, local 3D video retrieval is performed by dynamic time warping (DTW) algorithm in the feature space vectors. Experiments on both synthetic and real 3D human video sequences show that our approach provides an accurate shape similarity in video compared to the best state-of-the-art approaches. Finally, results on motion retrieval are promising and show the potential of this approach.
3D Mesh Unfolding via Semidefinite Programming
(The Eurographics Association, 2017) Liu, Juncheng; Lian, Zhouhui; Xiao, Jianguo; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
Mesh unfolding is a powerful pre-processing tool for many tasks such as non-rigid shape matching and retrieval. Shapes with articulated parts may exist large variants in pose, which brings difficulties to those tasks. With mesh unfolding, shapes in different poses can be transformed into similar canonical forms, which facilitates the subsequent applications. In this paper, we propose an automatic mesh unfolding algorithm based on semidefinite programming. The basic idea is to maximize the total variance of the vertex set for a given 3D mesh, while preserving the details by minimizing locally linear reconstruction errors. By optimizing a specifically-designed objective function, vertices tend to move against each other as far as possible, which leads to the unfolding operation. Compared to other Multi-Dimensional Scaling (MDS) based unfolding approaches, our method preserves significantly more details and requires no geodesic distance calculation. We demonstrate the advantages of our algorithm by performing 3D shape matching and retrieval in two publicly available datasets. Experimental results validate the effectiveness of our method both in visual judgment and quantitative comparison.
3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor
(The Eurographics Association, 2008) Papadakis, Panagiotis; Pratikakis, Ioannis; Theoharis, Theoharis; Passalis, Georgios; Perantonis, Stavros; Stavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmann
Abstract We present a novel 3D object retrieval method that relies upon a hybrid descriptor which is composed of 2D features based on depth buffers and 3D features based on spherical harmonics. To compensate for rotation, two alignment methods, namely CPCA and NPCA, are used while compactness is supported via scalar feature quantization to a set of values that is further compressed using Huffman coding. The superior performance of the proposed retrieval methodology is demonstrated through an extensive comparison against state-of-the-art methods on standard datasets.
3D Object Retrieval via Range Image Queries based on SIFT descriptors on Panoramic Views
(The Eurographics Association, 2012) Sfikas, Konstantinos; Pratikakis, Ioannis; Theoharis, Theoharis; M. Spagnuolo and M. Bronstein and A. Bronstein and A. Ferreira
The increasing availability of low-cost 3D scanners is resulting in the creation of large repositories of 3D models. Low-cost 3D range scanners in particular can also be used to capture partial views of real 3D objects which can then act as queries over 3D object repositories. This paper concerns a new methodology for 3D object retrieval based on range image queries which represent partial views of 3D objects. SIFT descriptors based on panoramic views are used to address this problem. The proposed method is evaluated against state-of-the-art works on a standard dataset.
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 Object Retrieval with Multimodal Views
(The Eurographics Association, 2015) Gao, Yue; Liu, Anan; Nie, Weizhi; Su, Yuting; Dai, Qionghai; Chen, Fuhai; Chen, Yingying; Cheng, Yanhua; Dong, Shuilong; Duan, Xingyue; Fu, Jianlong; Gao, Zan; Guo, Haiyun; Guo, Xin; Huang, Kaiqi; Ji, Rongrong; Jiang, Yingfeng; Li, Haisheng; Lu, Hanqing; Song, Jianming; Sun, Jing; Tan, Tieniu; Wang, Jinqiao; Yin, Huanpu; Zhang, Chaoli; Zhang, Guotai; Zhang, Yan; Zhang, Yan; Zhao, Chaoyang; Zhao, Xin; Zhu, Guibo; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
This paper reports the results of the SHREC'15 track: 3D Object Retrieval with Multimodal Views, which goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 505 objects is generated and both the color images and the depth images are provided for each object. 311 objects are selected as the queries and average retrieval performance is measured. The track attracted six participants and the submission of 26 runs, to two tasks. The evaluation results show a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data.
3D Object Retrieval with Parametric Templates
(The Eurographics Association, 2015) Getto, Roman; Fellner, Dieter W.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
We propose a 3D object retrieval system which uses parametric templates as prior knowledge for the retrieval. A parametric template represents an object-domain and a semantic concept like 'chair' or 'plane' or a more specific concept like 'dining-char' or 'biplane'. The template can be specified at a general or specific level and can even equal actual retrieved objects. The parametric template is composed of several input parameters and an operation chain which constructs an object. Different parameter combinations lead to different object instances. We combine and evaluate a paramteric template with different descriptors. Our results show that the usage of parametric templates can raise the retrieval performance significantly.
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.
A 3D Shape Benchmark for Retrieval and Automatic Classification of Architectural Data
(The Eurographics Association, 2009) Wessel, Raoul; Blümel, Ina; Klein, Reinhard; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
When drafting new buildings, architects make intensive use of existing 3D models including building elements, furnishing, and environment elements. These models are either directly included into the draft or serve as a source for inspiration. To allow efficient reuse of existing 3D models, shape retrieval methods considering the specific requirements of architects must be developed. Unfortunately, common 3D shape benchmarks which are used to evaluate the performance of retrieval algorithms are not well suited for architectural data. First, they incorporate models which are not related to this domain, and second and even more important, the provided classification schemes usually do not match an architect's intuition regarding their notion of design and function. To overcome these drawbacks, we present a freely downloadable shape benchmark especially designed for architectural 3D models. It currently contains 2257 objects from various content providers, including companies specialized on 3D CAD applications. All models are classified according to a scheme developed in close cooperation with architects taking into account their specific requirements regarding design and function. Additionally, we show retrieval results for this benchmark using unsupervised and supervised shape retrieval methods and discuss the specific problems regarding retrieval of architectural 3D models.
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.
3D Volume Matching for Mesh Animation of Moving Actors
(The Eurographics Association, 2014) Blache, Ludovic; Loscos, Celine; Nocent, Olivier; Lucas, Laurent; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
4D multiview reconstruction of moving actors has many applications in the entertainment industry and although studios providing such services become more accessible, efforts have to be done in order to improve the underlying technology to produce high-quality 4D contents. In this paper, we enable surface matching for an animated mesh sequence in order to introduce coherence in the data. The context is provided by an indoor multi-camera system which performs synchronized video captures from multiple viewpoints in a chroma key studio. Our input is given by a volumetric silhouette-based reconstruction algorithm that generates a visual hull at each frame of the video sequence. These 3D volumetric models differ from one frame to another, in terms of structure and topology, which makes them very difficult to use in post-production and 3D animation software solutions. Our goal is to transform this input sequence of independent 3D volumes into a single dynamic volumetric structure, directly usable in post-production. These volumes are then transformed into an animated mesh. Our approach is based on a motion estimation procedure. An unsigned distance function on the volumes is used as the main shape descriptor and a 3D surface matching algorithm minimizes the interference between unrelated surface regions. Experimental results, tested on our multiview datasets, show that our method outperforms approaches based on optical flow when considering robustness over several frames.
3D-Model Retrieval Using Bag-of-Features Based on Closed Curves
(The Eurographics Association, 2013) Khoury, Rachid El; Vandeborre, Jean-Philippe; Daoudi, Mohamed; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
Bag-of-feature technique is a popular approach in areas of computer vision and pattern recognition. Recently, it plays an important role in shape analysis community and especially in 3D-model retrieval. We present our approach for partial 3D-model retrieval using this technique based on closed curves. We define an invariant scalar function on the surface based on the commute-time distance. Our mapping function respects important properties in order to compute robust closed curves. Each scale of our scalar function detects a small region. The form of these regions are encoded in the form of the closed curves. We generate a collection of closed curves from a source point detected automatically. Based on the collection of all closed curves extracted, we construct our bag-of-features. Then we cluster the bag-of-features in the sense in accurate categorization. The centres of classes are defined as keyshapes. This method is particularly interesting in the sense of quantifying the 3D-model by its keyshapes that are accumulated into an histogram. The results shows the robustness of our method (BOF) compared to a method based on indexed closed curves (ICC) on various 3D-models with different poses.
3DOR 2016: Frontmatter
(Eurographics Association, 2016) Alfredo Ferreira; Andrea Giachetti; Daniela Giorgi;
3DOR 2017: Frontmatter
(Eurographics Association, 2017) Pratikakis, Ioannis; Dupont, Florent; Ovsjanikov, Maks;

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