Essays about: "point cloud segmentation"

Showing result 1 - 5 of 12 essays containing the words point cloud segmentation.

  1. 1. 3D Instance Segmentation of Cluttered Scenes : A Comparative Study of 3D Data Representations

    University essay from Linköpings universitet/Datorseende

    Author : Albin Konradsson; Gustav Bohman; [2021]
    Keywords : Deep Learning; Computer Vision; Point Cloud; Depth Map; 3D; Instance Segmentation; Cluttered Scenes;

    Abstract : This thesis provides a comparison between instance segmentation methods using point clouds and depth images. Specifically, their performance on cluttered scenes of irregular objects in an industrial environment is investigated. Recent work by Wang et al. READ MORE

  2. 2. Toward localization and mapping with heterogeneous depth sensors

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Paula Carbó Cubero; [2020]
    Keywords : ;

    Abstract : Heterogeneous collaborative Simultaneous Localization and Mapping (SLAM) can be defined as the solution to the SLAM problem that can handle different devices with different sensors, such as a monocular camera and a 3D LiDAR sensor, building a map and performing localization all at the same time. Research regarding this field is still at a very early stage, and it is hard to find solutions to the data association problem for these different types of sensor outputs. READ MORE

  3. 3. Deep Learning for Semantic Segmentation of 3D Point Clouds from an Airborne LiDAR

    University essay from Linköpings universitet/Datorseende

    Author : Sabina Serra; [2020]
    Keywords : Deep Learning; Machine Learning; Computer vision; Semantic Segmentation; Unmanned Aerial Vehicle; UAV; LiDAR; Point Cloud; 3D Data; Point Cloud Segmentation; Point Classification; Pre-training; Convolutional Neural Network; CNN; Djupinlärning; maskininlärning; semantisk segmentering; LiDAR; datorseende; punktmoln;

    Abstract : Light Detection and Ranging (LiDAR) sensors have many different application areas, from revealing archaeological structures to aiding navigation of vehicles. However, it is challenging to interpret and fully use the vast amount of unstructured data that LiDARs collect. READ MORE

  4. 4. Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data : Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data

    University essay from Linköpings universitet/Datorseende

    Author : Linbo He; [2019]
    Keywords : deep learning; multimodal fusion; multimodality; semantic segmentation; point cloud segmentation;

    Abstract : Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. READ MORE

  5. 5. Extracting masts of overhead supply and street lights from point cloud

    University essay from KTH/Skolan för arkitektur och samhällsbyggnad (ABE)

    Author : Yi Zhu; [2019]
    Keywords : Support Vector Machine; point cloud; laser scanning; automatic extraction;

    Abstract : Regular inspection and documentation for railway assets are necessary to monitor the status of the traffic environment. Mobile Laser Scanning (MLS) makes it possible to collect highly accurate spatial information of railway environments in the form of point cloud, and an automatic method to extract interested objects from the point cloud is needed to avoid too much manual work. READ MORE