3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studie

Attention Network for 3D Object Detection in Point Clouds

International audienceAccurate detection of objects in 3D point clouds is a central problem for autonomous navigation. Most existing methods use techniques of handcrafted features representation or multi-modal approaches prone to sensor failure. Approaches like PointNet that directly operate on sparse point data have shown good accuracy in the classification of single 3D objects. However, LiDAR sensors on Autonomous vehicles generate a large scale pointcloud. Real-time object detection in such a cluttered environment still remains a challenge. In this thesis, we propose Attentional PointNet, a novel end-toend trainable deep architecture for object detection in point clouds. We extend the theory of visual attention mechanism to 3D point clouds and introduce a new recurrent 3D Spatial Transformer Network module. Rather than processing whole point cloud, the network learns "where to look" (find regions of interest), thus significantly reducing the number of points and hence, inference time. Evaluation on KITTI car detection benchmark shows that our Attentional PointNet is notably faster and achieves comparable results with state-of-the-art LiDAR-based 3D detection methods.La détection précise d’objets dans un nuage de points 3D est un problème central pour la navigation autonome. La plupart des méthodes existantes utilisent des caractéristiques sélectionnées à la main ou des approches multimodèlessujettes à une défaillance du capteur. Des approches, telles que PointNet fonctionnant directement sur des données ponctuelles éparses, classifient précisément un nuage de points associé à un unique objet. Cependant, les capteurs Lidars sur les véhicules autonomes génèrent un nuage de points contenant de nombreux objets. Leurs détections en temps réel dans un environnement aussi encombré restent un défi. Dans cette thèse, nous proposons une méthode appelée Attentional PointNet, une architecture profonde complète, formable de bout en bout, destinée à la détection d’objets dans le nuage de points. Nous étendons la théorie du mécanisme d’attention visuelle au nuage de points 3D et introduisons un nouveau module récurrent de réseau de transformateur spatial 3D. Plutôt que de traiter le nuage de points dans sont ensemble, il apprend à reconnaître des régions potentiellement intéressantes. Ensuite, localiser des objets dans ces régions réduit considérablement le nombre de points à traiter et réduit le temps de calcul. L’évaluation avec les données du jeu de données KITTI montre que notre méthode est plus rapide et permet d’obtenir des résultats comparables avec les méthodes classiques de détection 3D utilisant des nuages de points générés par des Lidars

Quantitative Analysis of Saliency Models

Previous saliency detection research required the reader to evaluate performance qualitatively, based on renderings of saliency maps on a few shapes. This qualitative approach meant it was unclear which saliency models were better, or how well they compared to human perception. This paper provides a quantitative evaluation framework that addresses this issue. In the first quantitative analysis of 3D computational saliency models, we evaluate four computational saliency models and two baseline models against ground-truth saliency collected in previous work.Comment: 10 page

Autonomous robotic system for thermographic detection of defects in upper layers of carbon fiber reinforced polymers

Carbon Fiber Reinforced Polymers (CFRPs) are composites whose interesting properties, like high strength-to-weight ratio and rigidity, are of interest in many industrial fields. Many defects affecting their production process are due to the wrong distribution of the thermosetting polymer in the upper layers. In this work, they are effectively and efficiently detected by automatically analyzing the thermographic images obtained by Pulsed Phase Thermography (PPT) and comparing them with a defect-free reference. The flash lamp and infrared camera needed by PPT are mounted on an industrial robot so that surfaces of CFRP automotive components, car side blades in our case, can be inspected in a series of static tests. The thermographic image analysis is based on local contrast adjustment via UnSharp Masking (USM) and takes also advantage of the high level of knowledge of the entire system provided by the calibration procedures. This system could replace manual inspection leading to a substantial increase in efficiency

Exploiting low-cost 3D imagery for the purposes of detecting and analyzing pavement distresses

Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS

MRI image segmantation based on edge detection

Cílem této práce je představit základní segmentační techniky používáné v oblasti medicínského zpracování obrazových dat a pomocí 3D prohlížeče schopného zobrazit 3D obrazy implementovat segmentační modul založený na hranové detekci a vyhodnotit výsledky. Navrhovaný prohlížeč je sestavený v prostředi Matlab GUI a je schopen načíst objem 3D snímků představující lidskou hlavu. Navrhovaný segmentační modul je založen na použití hranových detektorů, zejména Cannyho detektoru.The aim of this thesis is to present the basic segmentation techniques uses in the field of medical image processing and by using a 3D viewer able to visualize 3D images, implement a segmentation module based on edges detection and evaluate the results. The proposed viewer is a 3D viewer build using matlab GUI and is able to load a volume of images representing the human head. The proposed segmentation module is based on the use of edge detectors particularly the Canny algorithm.