Sökning: "Point Cloud Segmentation"

Visar resultat 21 - 25 av 34 uppsatser innehållade orden Point Cloud Segmentation.

  1. 21. Automatiskt genererade dataset med SfM : En undersökning av SfM och dess egenskaper

    Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

    Författare :Jonas Elmesten; [2021]
    Nyckelord :A.I; LiDAR; SfM; point could; dataset; A.I; LiDAR; SfM; punktmoln; dataset.;

    Sammanfattning : Fler och fler industrier vänder blickarna mot A.I. (artificiell intelligens) för att undersöka om och hur det kan användas för att effektivisera olika processer. Men för att träna upp en A. LÄS MER

  3. 22. Toward localization and mapping with heterogeneous depth sensors

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Paula Carbó Cubero; [2020]
    Nyckelord :;

    Sammanfattning : 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. LÄS MER

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

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Sabina Serra; [2020]
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER

  5. 24. Automatiserad mönsterigenkänning av stenmurar

    Kandidat-uppsats, Högskolan i Gävle/Avdelningen för datavetenskap och samhällsbyggnad

    Författare :Adam Bergström; David Larsson; [2019]
    Nyckelord :Automatic Pattern Recognition; Stone Walls; Dense Forest; LiDAR; SPL100; Automatisk mönsterigenkänning; Stenmurar; Tät skog; Li-DAR; SPL100;

    Sammanfattning : Automated pattern recognition of stone walls, within both point cloud and image processing, can help identify previously inaccessible areas than with only image pro-cessing. This is important as stone walls are biotopes and serve as structures and have ecological functions for both plants and animals. LÄS MER

  7. 25. 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

    Master-uppsats, Linköpings universitet/Datorseende

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

    Sammanfattning : 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. LÄS MER