9,640 research outputs found
Special Issue “Analysis and Experimental Study on Natural Gas Hydrate Exploitation Processes”
No full textGas hydrates are crystalline structures formed by water molecule cages hosting gas molecules [...
Accurate and automatic alignment of range surfaces
No full textThis paper describes an automatic pipeline that is able to take a set of unordered range images and align them into a full 3D model. A global voting scheme is employed for view matching, inspired by 2D techniques for image mo- saicing. Then a multiple view registration approach is in- troduced, which aims at optimizing the alignment error si- multaneously for all the views. Experiments demonstrate the effectiveness of the method
The bag of words approach for retrieval and categorization of 3D objects
No full textIn this paper, we propose a novel framework for 3D object retrieval and categorization. The object is modeled in terms of its subparts as an histogram of 3D visual word occurrences. We introduce an effective method for hierarchical 3D object segmentation driven by the minima rule that combines spectral clustering-for the selection of seed-regions-with region growing based on fast marching. Descriptors attached to the regions allow the definition of the visual words. After coding of each object according to the Bag-of-Words paradigm, retrieval can be performed by matching with a suitable kernel, or categorization by learning a Support Vector Machine. Several examples on the AimShape watertight dataset and on the Tosca dataset demonstrate the versatility of the proposed method in working with either 3D objects with articulated shape changes or partially occluded or compound objects. Results are encouraging as shown by the comparison with other methods for each of the analyzed scenarios
A bag of words approach for 3D object categorization
No full textIn this paper we propose a novel framework for 3D object categorization. The object is modeled it in terms of its sub-parts as an histogram of 3D visual word occurrences. We introduce an effective method for hierarchical 3D object segmentation driven by the minima rule that combines spectral clustering - for the selection of seed-regions - with region growing based on fast marching. The front propagation is driven by local geometry features, namely the Shape Index. Finally, after the coding of each object according to the Bag-of-Words paradigm, a Support Vector Machine is learnt to classify different objects categories. Several examples on two different datasets are shown which evidence the effectiveness of the proposed framewor
Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning
No full textThe continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery; therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this article, we present novel weakly supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset, which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called cluster centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyperparameters related to feature extraction and network architecture is investigated. We find that the proposed SAE-based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyperparameter settings on all performance measures
Automatic Multi-view Surface Matching
No full textIn this paper we tackle the problem of automatically aligning an unordered set of range views. We propose a full pipeline that goes from the scans to the complete 3D model. The emphasis is on the automation – no manual in- tervention is require – and on the fact that no knowledge on the acquisition sequence is assumed. The contribution is twofold: in the pre-alignment phase a voting scheme is proposed that discovers the overlapping relationship among views; in the final refinement step we extend the Levenberg Marquardt-ICP to work with multiple views, in order to solve for the absolute pose of all images simultaneously
Visual vocabulary signature for 3D object retrieval and partial matching
No full textIn this paper a novel object signature is proposed for 3D object retrieval and partial matching. A part-based representation is obtained by partitioning the objects into subparts and by characterizing each segment with different geometric descriptors. Therefore, a Bag ofWords framework is introduced by clustering properly such descriptors in order to define the so called 3D visual vocabulary. In this fashion, the object signature is defined as a histogram of 3D visual word occurrences. Several examples on the Aim@Shape watertight dataset demonstrate the versatility of the proposed method in matching either 3D objects with articulated shape changes or partially occluded or compound objects. In particular, a comparison with the methods that participated to the Shape Retrieval contest 2007 (SHREC) reports satisfactory results for both object retrieval and partial matching
Registration of multiple acoustic range views for underwater scene reconstruction
No full textThis paper proposes a technique for the three-dimensional reconstruction of an underwater environment from multiple acoustic range views acquired by a remotely operated vehicle. The problem is made challenging by the very noisy nature of the data, the low resolution and the narrow field of view. Our main contribution is a new global registration technique to distribute registration errors evenly across all views. Our approach does not use data points after the first pairwise registration, for it works only on the transformations. Therefore, it is fast and occupies only a small memory. Experimental results suggest the global registration technique is effective in equalizing the error. Moreover, we introduce a statistically sound thresholding (the X84 rejection rule) to improve ICP robustness against noise and non-overlapping data
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