The increasing interest for reliable generation of large scale scenes and objects has facilitated several real-time applications. Although the resolution of the new generation geometry scanners are constantly improving, the output models, are inevitably noisy, requiring sophisticated approaches that remove noise while preserving sharp features. Moreover, we no longer deal exclusively with individual shapes, but with entire scenes resulting in a sequence of 3D surfaces that are affected by noise with different characteristics due to variable environmental factors (e.g., lighting conditions, orientation of the scanning device). In this work, we introduce a novel coarse-to-fine graph spectral processing approach that exploits the fact that the sharp features reside in a low dimensional structure hidden in the noisy 3D dataset. In the coarse step, the mesh is processed in parts, using a model based Bayesian learning method that identifies the noise level in each part and the subspace where the features lie. In the feature-aware fine step, we iteratively smooth face normals and vertices, while preserving geometric features. Extensive evaluation studies carried out under a broad set of complex noise patterns verify the superiority of our approach as compared to the state-of-the-art schemes, in terms of reconstruction quality and computational complexity.

Asthma and chronic obstructive pulmonary disease are obstructive respiratory diseases that affect negatively the quality of life for patients and their families worldwide. Despite the significance of these diseases, their management has been considered suboptimal around the world, whereas the improper inhaler use has been underlined as one of the main causes. Toward this direction, this paper presents an integrated mHealth system that provides real-time personalized feedback to patients for assessing the proper medication use and educating them and helping them avoid common mistakes. The identification of proper inhaler use is based on conventional and data-driven feature extraction and classification methods employed for the identification of four events (inhaler actuation, inhalation, exhalation, and background noise). The proposed scheme reaches 98% classification accuracy significantly outperforming recent and relevant state-of-the-art approaches. Finally, intuitive feedback interfaces were implemented in the form of a virtual guidance agent integrated with the mobile application, which can help patients follow their action plan and assess their inhaler technique in a more engaging manner. Extensive simulation studies, carried out using 12 subjects, demonstrated the efficiency of the proposed approaches in both indoor and outdoor environments.

Recent advances in 3-D scanning technology have enabled the deployment of 3-D models in various industrial applications such as digital twins, remote inspection, and reverse engineering. Despite their evolving performance, 3-D scanners still introduce noise and artifacts in the acquired dense models. In this article, we propose a fast and robust denoising method for the dense 3-D scanned industrial models. The proposed approach employs conditional variational autoencoders to effectively filter face normals. Training and inference are performed in a sliding patch setup reducing the size of the required training data and execution times. We conducted extensive evaluation studies using 3-D scanned and CAD models. The results verify plausible denoising outcomes, demonstrating similar or higher reconstruction accuracy, compared to other state-of-the-art approaches. Specifically, for 3-D models with more than 1e4 faces, the presented pipeline is twice as fast as methods with equivalent reconstruction error.

The 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.

Autonomous vehicles are expected to operate safely in real-life road conditions in the next years. Nevertheless, unanticipated events such as the existence of unexpected objects in the range of the road, can put safety at risk. The advancement of sensing and communication technologies and Internet of Things may facilitate the recognition of hazardous situations and information exchange in a cooperative driving scheme, providing new opportunities for the increase of collaborative situational awareness. Safe and unobtrusive visualization of the obtained information may nowadays be enabled through the adoption of novel Augmented Reality (AR) interfaces in the form of windshields. Motivated by these technological opportunities, we propose a saliency-based distributed, cooperative and rendering scheme for increasing the driver’s situational awareness through (i) automated negative obstacle (potholes) detection, (ii) AR visualization and (iii) information sharing (upcoming potential dangers) with other connected vehicles or road infrastructure. An extensive evaluation study using a variety of real datasets for pothole detection showed that the proposed method provides favorable results and features compared to other recent and relevant approaches.

Nowadays, the preservation and maintenance of historical objects is the main priority in the area of the heritage culture. The new generation of 3D scanning devices and the new assets of technological improvements have created a fertile ground for developing tools that could facilitate challenging tasks which traditionally required a huge amount of human effort and specialized knowledge of experts (e.g., a detailed inspection of defects in a historical object due to aging). These tasks demand more human effort, especially in some special cases, such as the inspection of a large-scale or remote object (e.g., tall columns, the roof of historical buildings, etc.), where the preserver expert does not have easy access to it. In this work, we propose a saliency aware compression and simplification framework for efficient remote inspection of Structure From Motion (SFM) reconstructed heritage 3D models. More specifically, we present a Convolutional Neural Network (CNN) based saliency map extraction pipeline that highlights the most important information of a 3D model.These include geometric details such as the fine features of the model or surface defects. An extensive experimental study, using a large number of real SFM reconstructed heritage 3D models, verifies the effectiveness and the robustness of the proposed method providing very promising results and draws future directions.