Aggressive saliency-aware point cloud compression

The increasing demand for accurate representations of 3D scenes, combined with immersive technologies has led point clouds to extensive popularity. However, quality point clouds require a large amount of data and therefore the need for compression methods is imperative. In this paper, we present a novel, geometry-based, end-to-end compression scheme, that combines information on the geometrical features of the point cloud and the user's position, achieving remarkable results for aggressive compression schemes demanding very small bit rates. After separating visible and non-visible points, four saliency maps are calculated, utilizing the point cloud's geometry and distance from the user, the visibility information, and the user's focus point. A combination of these maps results in a final saliency map, indicating the overall significance of each point and therefore quantizing different regions with a different number of bits during the encoding process. The decoder reconstructs the point cloud making use of delta coordinates and solving a sparse linear system. Evaluation studies and comparisons with the geometry-based point cloud compression (G-PCC) algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety of point clouds, demonstrate that the proposed method achieves significantly better results for small bit rates

Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusion approach to assess the operator's posture in real-time and to help avoid uncomfortable or unsafe positions. The system adjusts the robot's movements and informs the operator of any incorrect or potentially harmful postures, reducing the risk of accidents, strain, and musculoskeletal disorders. The analysis is personalized, taking into account the unique anthropometric characteristics of each operator, to ensure optimal ergonomics. The results of our experiments show that the proposed approach leads to improved human body postures and offers a promising solution for enhancing the ergonomics of operators in collaborative tasks

SHREC’20 Track:Retrieval of digital surfaces with similar geometric reliefs

International audienceThis paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and 1 the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches

Φασματική επεξεργασία και αλγόριθμοι βελτιστοποίησης σε στατικά και δυναμικά αντικείμενα τρισδιάστατης γεωμετρίας

Geometry processing of 3D objects is of primary interest in many areas of computer vision and graphics, including robot navigation, 3D object recognition, classification, feature extraction, etc. The recent introduction of low cost range sensors has created a great interest in many new areas, driving the need for developing efficient algorithms for 3D object processing. The current approaches of 3D object processing require a significant amount of manual intervention and they are still time-consuming making them unavailable for use in real-time applications. The aim of this thesis is to present algorithms, mainly inspired by the spectral analysis, subspace tracking and modern learning-based solutions, that can be used and facilitate many areas of low-level 3D geometry processing (i.e., reconstruction, outliers removal, denoising, compression), pattern recognition tasks (i.e., significant features extraction) and high-level applications (i.e., registration and identification of 3D objects in partially scanned and cluttered scenes), taking into consideration different types of 3D models (i.e., static and dynamic point clouds, static and dynamic 3D meshes).Η γεωμετρική επεξεργασία τρισδιάστατων αντικειμένων έχει μεγάλο ερευνητικό ενδιαφέρον σε ένα ευρύ φάσμα εφαρμογών που σχετίζονται με την υπολογιστική όραση και τα γραφικά, όπως για παράδειγμα στην αυτόματη κίνηση robot στον χώρο, την αναγνώριση τρισδιάστατων αντικειμένων, την κατηγοριοποίηση, την εξαγωγή χαρακτηριστικών και άλλες. Η δημιουργία νέας γενιάς οικονομικών αισθητήρων, που έχουν αναπτυχθεί τα τελευταία χρόνια, έχει παίξει σημαντικό ρόλο στην προσέλκυση ενδιαφέροντος από διάφορες περιοχές, οι οποίες έχουν σκοπό να αναπτύξουν εφαρμογές που να κάνουν χρήση της 3D πληροφορίας επωφελούμενες των πλεονεκτημάτων που προσφέρει μια πιο ολοκληρωμένη αναπαράσταση του κόσμου (3D αναπαράσταση) σε σύγκριση με την εικόνα και το video. Οι υπάρχουσες προσεγγίσεις που σχετίζονται με την επεξεργασία 3D αντικειμένων, απαιτούν, σε σημαντικό βαθμό, ειδικές παρεμβάσεις από τους χρήστες και επίσης είναι αρκετά χρονοβόρες για να χρησιμοποιηθούν σε εφαρμογές πραγματικού χρόνου. Στόχος της παρούσας διατριβής είναι να παρουσιάσει αλγορίθμους που μπορούν να χρησιμοποιηθούν αποτελεσματικά και ωφέλιμα σε πλήθος χαμηλού επιπέδου εφαρμογών που αφορούν την επεξεργασία 3D γεωμετρίας όπως (ανακατασκευή επιφάνειας, απομάκρυνση έκτοπων σημείων, αποθορυβοποίηση, συμπίεση) σε εργασίες που αφορούν την αναγνώριση προτύπων (εντοπισμός σημαντικών σημείων και χαρτογράφηση βάση γεωμετρικής σημαντικότητας) καθώς και σε υψηλού επιπέδου εφαρμογές (εντοπισμός 3Dαντικειμένων σε σκηνές μερικής αποτύπωσης), λαμβάνοντας επίσης υπόψην διαφορετικούς τύπους 3Dμοντέλων (στατικά και δυναμικά νέφη σημείων, στατικά και δυναμικά 3D αντικείμενα πλεγματικής αναπαράστασης)

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking of the graph Laplacian eigenspaces. Additionally, we present a dynamic version that automatically identifies the optimal subspace size that satisfies a given reconstruction quality threshold. In this way, we overcome the problem of the perceptual distortions, due to the fixed number of subspace sizes that is used for all the separated parts individually. Extensive simulations carried out using different 3D models in different use cases (i.e., compression and denoising), showed that the proposed approach is very fast, especially in comparison with the SVD based spectral processing approaches, while at the same time the quality of the reconstructed models is of similar or even better reconstruction quality. The experimental analysis also showed that the proposed approach could also be used by other denoising methods as a preprocessing step, in order to optimize the reconstruction quality of their results and decrease their computational complexity since they need fewer iterations to converge

Registrace a identifikace 3D objektů v částečně nasnímaných a zaplněných scénách metodou postupného zpřesnění

3D snímací zařízení nové generace přinesly revoluci ve způsobu jakým se získává informace ze 3D objektů, čímž se proces zachycení a digitalizace scény stal přímočarým. Nicméně efektivnost a robustnost konvenčních algoritmů pro analýzu reálných scén obvykle zaostává v případě náročných podmínek, jako může být přítomnost šumu, nízké rozlišení a špatná perceptuální kvalita. V této práci prezentujeme metodologii pro identifikaci a registraci částečně nasnímaných a zašumělých 3D objektů, nacházejících se v obecné poloze ve 3D scéně, s odpovídajícími modely vysoké kvality. Metodologie je vyhodnocena na mračnech bodů s mnoha objekty a chybějícími částmi. Navrhovaný přístup nevyžaduje informace o konektivitě a je tedy obecný a výpočetně efektivní, čímž usnadňuje výpočetně náročné aplikace, jako je rozšířená realita. Hlavními přínosy této práce je zavedení vrstveného společného registračního a indexačního schématu nepřehledných dílčích mračen bodů pomocí nové víceúrovňové techniky extrakce význačnosti k identifikaci charakteristických oblastí a vylepšeného kritéria podobnosti pro párování mezi objektem a modelem. Doba zpracování procesu je také urychlena díky 3D segmentaci scény. Srovnání navržené metodiky s jinými nejmodernějšími přístupy zdůrazňují její přednosti v náročných podmínkáchThe new generation 3D scanner devices have revolutionized the way information from 3D objects is acquired, making the process of scene capturing and digitization straightforward. However, the effectiveness and robustness of conventional algorithms for real scene analysis are usually deteriorated due to challenging conditions, such as noise, low resolution, and bad perceptual quality. In this work, we present a methodology for identifying and registering partially-scanned and noisy 3D objects, lying in arbitrary positions in a 3D scene, with corresponding high-quality models. The methodology is assessed on point cloud scenes with multiple objects with large missing parts. The proposed approach does not require connectivity information and is thus generic and computationally efficient, thereby facilitating computationally demanding applications, like augmented reality. The main contributions of this work are the introduction of a layered joint registration and indexing scheme of cluttered partial point clouds using a novel multi-scale saliency extraction technique to identify distinctive regions, and an enhanced similarity criterion for object-to-model matching. The processing time of the process is also accelerated through 3D scene segmentation. Comparisons of the proposed methodology with other state-of-the-art approaches highlight its superiority under challenging conditions

Fast and effective dynamic mesh completion

We introduce a novel approach to support fast and efficient completion of arbitrary animation sequences, ideally suited for real-time scenarios, such as immersive tele-presence systems and gaming. In most of these applications, the reconstruction of 3D animations is based on dynamic meshes which are highly incomplete, stressing the need of completion approaches with low computational requirements. In this paper, we present a new online approach for fast and effective completion of 3D animated models that estimates the position of the unknown vertices of the current frame by exploiting the connectivity information and the current motion vectors of the known vertices. Extensive evaluation studies carried out using a collection of different incomplete animated models, verify that the proposed technique achieves plausible reconstruction output despite the constraints posed by arbitrarily complex and motion scenarios

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation