3DOR 15

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https://diglib.eg.org/handle/10.2312/12281

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Bag of Compact HKS-based Feature Descriptors
(The Eurographics Association, 2015) ElNaghy, Hanan; Hamad, Safwat; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
3D object retrieval has become an integral part in many today's applications attracting extensive research efforts. This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor. The key idea is based on integrating Bag of features (BoF) paradigm with Heat Kernel Signature (HKS) for feature description and detection. Initially, HKS computation phase defines HKS point signatures for each 3D model. Then, an innovative feature point detection algorithm provides a succinct set of feature points to be associated with a compact HKS-based descriptor vectors computed at local time scales. Finally, we take advantage of the BoF paradigm to encode a given 3D model with an informative feature frequency vector. The proposed approach has been evaluated on SHREC 2015 dataset of non-rigid models. The experimental results demonstrate the effective retrieval performance, invariance to different kinds of deformation and possible noise.
Randomized Sub-Volume Partitioning for Part-Based 3D Model Retrieval
(The Eurographics Association, 2015) Furuya, Takahiko; Kurabe, Seiya; Ohbuchi, Ryutarou; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
Given a query that specifies partial shape, a Part-based 3D Model Retrieval (P3DMR) system would retrieve 3D models whose part(s) matches the query. Computationally, this is quite challenging; the query must be compared against parts of 3D models having unknown position, orientation, and scale. To our knowledge, no algorithm can perform P3DMR on a database having significant size (e.g., 100K 3D models) that includes polygon soup and other not-so-well-defined shape representations. In this paper, we propose a scalable P3DMR algorithm called Part-based 3D model retrieval by Randomized Sub-Volume Partitioning, or P3D-RSVP. To match a partial query with a set of (whole) 3D models in the database, P3D-RSVP iteratively partitions a 3D model into a set of sub-volumes by using 3D grids having randomized intervals and orientations. To quickly compare the query with all the sub-volumes of all the models in the database, P3D-RSVP hashes high dimensional features into compact binary codes. Quantitative evaluation using several benchmarks shows that the P3D-RSVP is able to query a 50K model database in 2 seconds.
Range Scans based 3D Shape Retrieval
(The Eurographics Association, 2015) Godil, A.; Dutagaci, H.; Bustos, B.; Choi, S.; Dong, S.; Furuya, T.; Li, H.; Link, N.; Moriyama, A.; Meruane, R.; Ohbuchi, R.; Paulus, D.; Schreck, T.; Seib, V.; Sipiran, I.; Yin, H.; Zhang, C.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
The objective of the SHREC'15 Range Scans based 3D Shape Retrieval track is to evaluate algorithms that match range scans of real objects to complete 3D mesh models in a target dataset. The task is to retrieve a rank list of complete 3D models that are of the same category given the range scan of a query object. This capability is essential to many computer vision systems that involves recognition and classification of objects in the environment based on depth information. In this track, the target dataset consists of 1200 3D mesh models and the query set has 180 range scans of 60 physical objects. Six research groups participated in the contest with a total of 16 different runs. This paper presents the track datasets, participants' methods and the results of the contest.
RETRIEVAL 3D: An On-line Content-Based Retrieval Performance Evaluation Tool
(The Eurographics Association, 2015) Koutsoudis, Anestis; Ioannakis, George; Pratikakis, Ioannis; Chamzas, Christos; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
Performance benchmarking is an absolute necessity when attempting to objectively quantify the performance of content-based retrieval methods. For many years now, a number of plot-based and scalar-based measures in combination with benchmark datasets have already been used in order to provide objective results. In this work, we present the first version of an integrated on-line content-based retrieval evaluation tool, named RETRIEVAL 3D, which can be used in order to quantify the performance of a retrieval method. The current version of the system offers a set of popular performance measures that can be accessed through a dynamic visualisation environment. The user is able to upload retrieval results using different input data structures (e.g. binary ranked lists, floating point ranked lists, dissimilarity matrices and groundtruth data) that are already encountered in the literature including the SHREC competition series. Moreover, the system is able to provide evaluation mechanisms for known within the retrieval research community benchmark datasets. It offers performance measures parameterisation that enables the user to determine specific aspects of the evaluated retrieval method. Performance reports archiving and downloading are some of the system's user-oriented functionalities.
Retrieval of Objects Captured with Kinect One Camera
(The Eurographics Association, 2015) Pascoal, Pedro B.; Proença, Pedro; Gaspar, Filipe; Dias, Miguel Sales; Teixeira, Filipe; Ferreira, Alfredo; Seib, Viktor; Link, Norman; Paulus, Dietrich; Tatsuma, Atsushi; Aono, Masaki; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
Low-cost RGB-D sensing technology, such as the Microsoft Kinect, is gaining acceptance in the scientific community and even entering into our homes. This technology enables ordinary users to capture everyday object into digital 3D representations. Considering the image retrieval context, whereas the ability to digitalize photos led to a rapid increase of large collections of images, which in turn raised the need of efficient search and retrieval techniques. We believe the same is happening now for the 3D domain. Therefore, it is essential to identify which 3D shape descriptors, provide better matching and retrieval of such digitalized objects. In this paper, we start by presenting a collection of 3D objects acquired using the latest version of Microsoft Kinect, namely, Kinect One. This dataset, comprising 175 common household objects classified into 18 different classes, was then used for the SHape REtrieval Contest (SHREC). Two groups have submitted their 3D matching techniques, providing the rank list with top 10 results, using the complete set of 175 objects as queries.
ThOR: Three-dimensional Object Retrieval Library
(The Eurographics Association, 2015) Pascoal, Pedro B.; Ferreira, Alfredo; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
Following the increasing number of 3D object collections, researchers developed several algorithms related to 3D object analysis, comparison and retrieval methods. However, there is no simple solution offering researchers and practitioners a framework for the integration of algorithms and techniques developed within this context into their applications and tools. ThOR (Three-dimensional Object Retrieval) is a lightweight open source Java library for content based object retrieval, that provides common 3D shape retrieval indexing and retrieval tools. Most important, it allows addition of new components, such as shape descriptors, with minimal effort. In short, ThOR provides an easy solution for the implementation of 3D object retrieval tools using both local and internet-based client-server architectures.

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Browsing 3DOR 15 by Subject "H.3.3 [Information Storage and Retrieval]"

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