The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we tackle the labeling problem for 3D point clouds. We introduce a 3D point cloud labeling scheme based on 3D Convolutional Neural Network. Our approach minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did. Particularly, we present solutions for large data handling during the training...
Dual energy computed tomography (DECT) has improved capability of differentiating different materials compared to conventional CT. However, due to non-negligible radiation exposure to patients, dose reduction has recently become a critical concern in CT imaging field. Moreover, direct material decomposition techniques such as numerical inversion can yield significantly amplified noise in the basic...
In this paper, we aim at detecting vehicles from the point clouds scanned from the urban area. Our detection method consists of a segmentation stage and a classification stage. Prior knowledge for vehicles and urban environment is utilized to help the detection process. Specifically, we incorporate curb detection and removal in the segmentation stage. Moreover, our approach is able to estimate the...
3D modeling of point clouds is an important but time-consuming process, inspiring extensive research in automatic methods. Prior efforts focus on primitive geometry, street structures or indoor objects, but industrial data has rarely been pursued. Our work presents a method for automatic modeling and recognition of 3D industrial site point clouds, dividing the task into 3 separate sub-problems: pipe...
This paper focuses on detecting and classifying pole-like objects from point clouds obtained in urban areas. To achieve our goal, we propose a system consisting of three stages: localization, segmentation and classification. The localization algorithm based on slicing, clustering, pole seed generation and bucket augmentation takes advantage of the unique characteristics of pole-like objects and avoids...
As laser scanners become widely used in 3D data acquisition of industrial sites, one challenging problem emerges: given two data of the same site scanned/modeled at different times, how can we tell the difference between the two? In this paper, we formulate this problem as the 3D change detection problem, and propose a novel method for detecting object-level changes. In general, we notice that the...
This paper focuses on detecting parts in laser-scanned data of a cluttered industrial scene. To achieve the goal, we propose a robust object detection system based on segmentation and matching, as well as an adaptive segmentation algorithm and an efficient pose extraction algorithm based on correspondence filtering. We also propose an overlapping-based criterion that exploits more information of the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.