DETECTION OF PERSONS IN MLS POINT CLOUDS
نویسندگان
چکیده
منابع مشابه
3D Detection of Power-Transmission Lines in Point Clouds Using Random Forest Method
Inspection of power transmission lines using classic experts based methods suffers from disadvantages such as highel level of time and money consumption. Advent of UAVs and their application in aerial data gathering help to decrease the time and cost promenantly. The purpose of this research is to present an efficient automated method for inspection of power transmission lines based on point c...
متن کاملShape Detection in Point Clouds
In this work we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders and cones. For models w...
متن کاملLine Segment-based Approach for Accuracy Assessment of Mls Point Clouds in Urban Areas
This paper presents an accuracy assessment of Mobile Laser Scanning (MLS) point clouds and the initial results of practical experiments carried out for a custom-built mobile system. To minimise the difficulties of identification and precise measurement of control points, an approach based on line segments is proposed. The main aim of this technique is to compare a series of 3D line segments ext...
متن کاملGrasp Pose Detection in Point Clouds
Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying idea is to treat grasp perception analogously to object detection in computer vision. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose est...
متن کاملMethods for Feature Detection in Point Clouds
This paper gives an overview over several techniques for detection of features, and in particular sharp features, on point-sampled geometry. In addition, a new technique using the Gauss map is shown. Given an unstructured point cloud, this method computes a Gauss map clustering on local neighborhoods in order to discard all points that are unlikely to belong to a sharp feature. A single paramet...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2017
ISSN: 2194-9034
DOI: 10.5194/isprs-archives-xlii-2-w7-203-2017