3DOR 17

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https://diglib.eg.org/handle/10.2312/2631242

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Browsing 3DOR 17 by Subject "Computational Geometry and Object Modeling"

Now showing 1 - 5 of 5
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    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.
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    Deformable Shape Retrieval with Missing Parts
    (The Eurographics Association, 2017) Rodolà, E.; Cosmo, L.; Litany, O.; Bronstein, M. M.; Bronstein, A. M.; Audebert, N.; Hamza, A. Ben; Boulch, A.; Castellani, U.; Do, M. N.; Duong, A.-D.; Furuya, T.; Gasparetto, A.; Hong, Y.; Kim, J.; Saux, B. Le; Litman, R.; Masoumi, M.; Minello, G.; Nguyen, H.-D.; Nguyen, V.-T.; Ohbuchi, R.; Pham, V.-K.; Phan, T. V.; Rezaei, M.; Torsello, A.; Tran, M.-T.; Tran, Q.-T.; Truong, B.; Wan, L.; Zou, C.; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    Partial similarity problems arise in numerous applications that involve real data acquisition by 3D sensors, inevitably leading to missing parts due to occlusions and partial views. In this setting, the shapes to be retrieved may undergo a variety of transformations simultaneously, such as non-rigid deformations (changes in pose), topological noise, and missing parts - a combination of nuisance factors that renders the retrieval process extremely challenging. With this benchmark, we aim to evaluate the state of the art in deformable shape retrieval under such kind of transformations. The benchmark is organized in two sub-challenges exemplifying different data modalities (3D vs. 2.5D). A total of 15 retrieval algorithms were evaluated in the contest; this paper presents the details of the dataset, and shows thorough comparisons among all competing methods.
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    Directed Curvature Histograms for Robotic Grasping
    (The Eurographics Association, 2017) Schulz, Rodrigo; Guerrero, Pablo; Bustos, Benjamin; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    Three-dimensional descriptors are a common tool nowadays, used in a wide range of tasks. Most of the descriptors that have been proposed in the literature focus on tasks such as object recognition and identification. This paper proposes a novel three-dimensional local descriptor, structured as a set of histograms of the curvature observed on the surface of the object in different directions. This descriptor is designed with a focus on the resolution of the robotic grasping problem, especially on the determination of the orientation required to grasp an object. We validate our proposal following a data-driven approach using grasping information and examples generated using the Gazebo simulator and a simulated PR2 robot. Experimental results show that the proposed descriptor is well suited for the grasping problem, exceeding the performance observed with recent descriptors.
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    LightNet: A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition
    (The Eurographics Association, 2017) Zhi, Shuaifeng; Liu, Yongxiang; Li, Xiang; Guo, Yulan; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    With the rapid growth of 3D data, accurate and efficient 3D object recognition becomes a major problem. Machine learning methods have achieved the state-of-the-art performance in the area, especially for deep convolutional neural networks. However, existing network models have high computational cost and are unsuitable for real-time 3D object recognition applications. In this paper, we propose LightNet, a lightweight 3D convolutional neural network for real-time 3D object recognition. It achieves comparable accuracy to the state-of-the-art methods with a single model and extremely low computational cost. Experiments have been conducted on the ModelNet and Sydney Urban Objects datasets. It is shown that our model improves the VoxNet model by relative 17.4% and 23.1% on the ModelNet10 and ModelNet40 benchmarks with less than 67% of training parameters. It is also demonstrated that the model can be applied in real-time scenarios.
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    Semantic Correspondence Across 3D Models for Example-based Modeling
    (The Eurographics Association, 2017) Léon, Vincent; Itier, Vincent; Bonneel, Nicolas; Lavoué, Guillaume; Vandeborre, Jean-Philippe; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    Modeling 3D shapes is a specialized skill not affordable to most novice artists due to its complexity and tediousness. At the same time, databases of complex models ready for use are becoming widespread, and can help the modeling task in a process called example-based modeling. We introduce such an example-based mesh modeling approach which, contrary to prior work, allows for the replacement of any localized region of a mesh by a region of similar semantics (but different geometry) within a mesh database. For that, we introduce a selection tool in a space of semantic descriptors that co-selects areas of similar semantics within the database. Moreover, this tool can be used for part-based retrieval across the database. Then, we show how semantic information improves the assembly process. This allows for modeling complex meshes from a coarse geometry and a database of more detailed meshes, and makes modeling accessible to the novice user.