3DOR 17
https://diglib.eg.org:443/handle/10.2312/2631242
ISBN 978-3-03868-030-72024-03-29T00:11:19ZDirected Curvature Histograms for Robotic Grasping
https://diglib.eg.org:443/handle/10.2312/3dor20171060
Directed Curvature Histograms for Robotic Grasping
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.
2017-01-01T00:00:00ZTowards Recognizing of 3D Models Using A Single Image
https://diglib.eg.org:443/handle/10.2312/3dor20171062
Towards Recognizing of 3D Models Using A Single Image
Rashwan, Hatem A.; Chambon, Sylvie; Morin, Geraldine; Gurdjos, Pierre; Charvillat, Vincent
Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
As 3D data is getting more popular, techniques for retrieving a particular 3D model are necessary. We want to recognize a 3D model from a single photograph; as any user can easily get an image of a model he/she would like to find, requesting by an image is indeed simple and natural. However, a 2D intensity image is relative to viewpoint, texture and lighting condition and thus matching with a 3D geometric model is very challenging. This paper proposes a first step towards matching a 2D image to models, based on features repeatable in 2D images and in depth images (generated from 3D models); we show their independence to textures and lighting. Then, the detected features are matched to recognize 3D models by combining HOG (Histogram Of Gradients) descriptors and repeatability scores. The proposed methods reaches a recognition rate of 72% among 12 3D objects categories, and outperforms classical feature detection techniques for recognizing 3D models using a single image.
2017-01-01T00:00:00ZSemantic Correspondence Across 3D Models for Example-based Modeling
https://diglib.eg.org:443/handle/10.2312/3dor20171061
Semantic Correspondence Across 3D Models for Example-based Modeling
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.
2017-01-01T00:00:00ZRetrieval of Surfaces with Similar Relief Patterns
https://diglib.eg.org:443/handle/10.2312/3dor20171058
Retrieval of Surfaces with Similar Relief Patterns
Biasotti, S.; Thompson, E. Moscoso; Aono, M.; Hamza, A. Ben; Bustos, B.; Dong, S.; Du, B.; Fehri, A.; Li, H.; Limberger, F. A.; Masoumi, M.; Rezaei, M.; Sipiran, I.; Sun, L.; Tatsuma, A.; Forero, S. Velasco; Wilson, R. C.; Wu, Y.; Zhang, J.; Zhao, T.; Fornasa, F.; Giachetti, A.
Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
This paper presents the results of the SHREC'17 contest on retrieval of surfaces with similar relief patterns. The proposed task was created in order to verify the possibility of retrieving surface patches with a relief pattern similar to an example from a database of small surface elements. This task, related to many real world applications, requires an effective characterization of local "texture" information not depending on patch size and bending. Retrieval performances of the proposed methods reveal that the problem is not quite easy to solve and, even if some of the proposed methods demonstrate promising results, further research is surely needed to find effective relief pattern characterization techniques for practical applications.
2017-01-01T00:00:00Z