3DOR 13

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/308

Browse

Now showing 1 - 15 of 15

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-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.
Automatic Shape Expansion with Verification to Improve 3D Retrieval, Classification and Matching
(The Eurographics Association, 2013) Knopp, Jan; Prasad, Mukta; Gool, Luc Van; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
The goal of this paper is to retrieve 3D object models from a database, that are similar to a single 3D object model, given as a query. The system has no prior models of any object class and is class-generic. The approach is fully automated and unsupervised. The main contribution of the paper is to improve the quality of such 3D shape retrieval, through query verification and query expansion. These are part of a cascaded, two-stage system: (i) Verification: after a first inexpensive and coarse retrieval step that uses a standard Bag-of-Words (BoW) over quantized local features, a fast but effective spatial layout verification of those words is used to prune the initial search results. (ii) Expansion: a new BoW query is issued on the basis of an expanded set of query shapes that, next to the original query, also includes the positively verified results of (i). We perform comprehensive evaluation and show improved performance. As an additional novelty, we show the usefulness of the query expansion on shape classification with limited training data and shape matching, domains in which it has not been used before. The experiments were performed on a variety of state-of-the-art datasets.
Charge Density-Based 3D Model Retrieval Using Bag-of-Feature
(The Eurographics Association, 2013) Alizadeh, Fattah; Sutherland, Alistair; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
As the number of 3D models is growing on the internet and other domain-specific datasets, the search and retrieval of such models are attracting a lot of attention. A shape descriptor it plays critical roles in the retrieval quality enhancement. In this paper we propose a new robust shape descriptor based on the distribution of charge density on the surface of a 3D model. After calculating the charge density for each triangular face of each model as local features, we utilize the Bag-of-Features framework to perform global matching using the local features. Our experiments on the McGill and PSB datasets show that the proposed descriptor is robust to a variety of modifications and transformations and offers a higher retrieving quality compared to other well-known approaches.
Compact Vectors of Locally Aggregated Tensors for 3D Shape Retrieval
(The Eurographics Association, 2013) Tabia, Hedi; Picard, David; Laga, Hamid; Gosselin, Philippe-Henri; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
During the last decade, a significant attention has been paid, by the computer vision and the computer graphics communities, to three dimensional (3D) object retrieval. Shape retrieval methods can be divided into three main steps: the shape descriptors extraction, the shape signatures and their associated similarity measures, and the machine learning relevance functions. While the first and the last points have vastly been addressed in recent years, in this paper, we focus on the second point; presenting a new 3D object retrieval method using a new coding/pooling technique and powerful 3D shape descriptors extracted from 2D views. For a given 3D shape, the approach extracts a very large and dense set of local descriptors. From these descriptors, we build a new shape signature by aggregating tensor products of visual descriptors. The similarity between 3D models can then be efficiently computed with a simple dot product. We further improve the compactness and discrimination power of the descriptor using local Principal Component Analysis on each cluster of descriptors. Experiments on the SHREC 2012 and the McGill benchmarks show that our approach outperforms the state-of-the-art techniques, including other BoF methods, both in compactness of the representation and in the retrieval performance.
Features Accumulation on a Multiple View Oriented Model for People Re-Identification
(The Eurographics Association, 2013) García, Jorge; Kambhamettu, C.; Gardel, A.; Bravo, I.; Lázaro, J. L.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
People re-identification process provides relevant information in order to understand the scene. In this paper, we present a multiple-view oriented model for performing people re-identification in a camera network. An appear- ance model for different perspectives is generated from people trajectories. Global and local features besides path orientation are extracted from each person's image, given a short-term tracking. Experimental results based on different sets of features demonstrate the effectiveness of our system.
Geometric Histograms of 3D Keypoints for Face Identification with Missing Parts
(The Eurographics Association, 2013) Berretti, Stefano; Werghi, Naoufel; Bimbo, Alberto del; Pala, Pietro; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.
Learning Kernels on Extended Reeb Graphs for 3D Shape Classification and Retrieval
(The Eurographics Association, 2013) Barra, Vincent; Biasotti, Silvia; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
This paper addresses 3D shape classification and retrieval in terms of supervised selection of the most significant features in a space of attributed graphs encoding different shape characteristics. For this purpose, 3D models are represented as bags of shortest paths defined over well chosen Extended Reeb graphs, while the similarity between pairs of Extended Reeb graphs is addressed through kernels adapted to these descriptions. Given this set of kernels, a Multiple Kernel Learning algorithm is used to find an optimal linear combination of kernels for classification and retrieval purposes. Results are comparable with the best results of the literature, and the modularity and flexibility of the kernel learning ensure its applicability to a large set of methods.
Local Signature Quantization by Sparse Coding
(The Eurographics Association, 2013) Boscaini, Davide; Castellani, Umberto; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterize geometric properties of the underlying surface. To this aim two main approaches are considered: global, and local ones. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Some strategies to combine these two approaches have been proposed recently but still no consolidate work is available in this field. With this paper we address this problem and propose a new method based on sparse coding techniques. A set of local shape descriptors are collected from the shape. Then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptor for shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drastic improvement of the proposed method in comparison with well known local-to-global and global approaches.
SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval
(The Eurographics Association, 2013) Li, B.; Lu, Y.; Godil, A.; Schreck, Tobias; Aono, M.; Johan, H.; Saavedra, J. M.; Tashiro, S.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of sketch-based 3D shape retrieval methods based on a large scale hand-drawn sketch query dataset which has 7200 sketches and a generic 3D model target dataset containing 1258 3D models. The sketches and models are divided into 80 distinct classes. In this track, 5 runs have been submitted by 3 groups and their retrieval accuracies were evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, its corresponding evaluation code, and the comparative evaluation results will contribute to the progress of this research direction for the 3D model retrieval community.
SHREC'13 Track: Large-Scale Partial Shape Retrieval Using Simulated Range Images
(The Eurographics Association, 2013) Sipiran, I.; Meruane, R.; Bustos, B.; Schreck, Tobias; Johan, H.; Li, B.; Lu, Y.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
Partial shape retrieval is a challenging problem in content-based 3D model retrieval. This track intends to evaluate the performance of existing algorithms for partial retrieval. The contest is based on a new large-scale query set obtained by mimicking the range image acquisition using a standard 3D benchmark as target set. The query set contains 7200 partial meshes with different levels of complexity. Furthermore, we propose the use of new performance measures based on a partiality factor. With this characteristics, our goal is to evaluate several important aspects: effectiveness, efficiency, robustness and scalability. The obtained results of this track open new questions regarding the difficulty of the partial shape retrieval problem and the scalability of algorithms. In addition, potential future directions on this topic are identified.
SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras
(The Eurographics Association, 2013) Machado, J.; Ferreira, A.; Pascoal, P. B.; Abdelrahman, M.; Aono, M.; El-Melegy, M.; Farag, A.; Johan, H.; Li, B.; Lu, Y.; Tatsuma, A.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set.
SHREC'13 Track: Retrieval on Textured 3D Models
(The Eurographics Association, 2013) Cerri, A.; Biasotti, S.; Abdelrahman, M.; Angulo, J.; Berger, K.; Chevallier, L.; El-Melegy, M.; Farag, A.; Lefebvre, F.; Giachetti, A.; Guermoud, H.; Liu, Y.-J.; Velasco-Forero, S.; Vigouroux, JR.; Xu, C.-X.; Zhang, J.-B.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
This contribution reports the results of the SHREC 2013 track: Retrieval on Textured 3D Models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 240 textured mesh models, divided into 10 classes. Each model has been used in turn as a query against the remaining part of the database. For a given query, the goal was to retrieve the most similar objects. The track saw six participants and the submission of eleven runs.
Sketch-Based 3D Model Retrieval by Viewpoint Entropy-Based Adaptive View Clustering
(The Eurographics Association, 2013) Li, Bo; Lu, Yijuan; Johan, Henry; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
Searching for relevant 3D models based on hand-drawn sketches is both intuitive and important for many applications, such as sketch-based 3D modeling and recognition.We propose a sketch-based 3D model retrieval algorithm by utilizing viewpoint entropy-based adaptive view clustering and shape context matching. Different models have different visual complexities, thus there is no need to keep the same number of representative views for each model. Motivated by this, we propose to measure the visual complexity of a 3D model by utilizing viewpoint entropy distribution of a set of sample views and based on the complexity value, we can adaptively decide the number of representative views. Finally, we perform Fuzzy C-Means based view clustering on the sample views based on their viewpoint entropy values. We test our algorithm on two latest sketch-based 3D model retrieval benchmarks and compare it with other four state-of-the-art approaches. The results demonstrate the superior performance and advantages of our algorithm.
SymPan: 3D Model Pose Normalization via Panoramic Views and Reflective Symmetry
(The Eurographics Association, 2013) Sfikas, Konstantinos; Pratikakis, Ioannis; Theoharis, Theoharis; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
A novel pose normalization method, based on panoramic views and reflective symmetry, is presented. Initially, the surface of a 3D model is projected onto the lateral surface of a circumscribed cylinder, aligned with the primary principal axis of space. Based on this cylindrical projection, a normals' deviation map is extracted and using an octree-based search strategy, the rotation which optimally aligns the primary principal axis of the 3D model and the cylinder's axis is computed. The 3D model's secondary principal axis is then aligned with the secondary principal axis of space in a similar manner. The proposed method is incorporated in a hybrid scheme, that serves as the pose normalization method in a state-of-the-art 3D model retrieval system. The effectiveness of this system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and the results clearly show improved performance against current approaches.

Browse

Browsing 3DOR 13 by Title

Results Per Page

Sort Options