Sökning: "point cloud segmentation"

Visar resultat 1 - 5 av 26 uppsatser innehållade orden point cloud segmentation.

  1. 1. Maximizing the performance of point cloud 4D panoptic segmentation using AutoML technique

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

    Författare :Teng Ma; [2022]
    Nyckelord :LiDAR perception; 4D panoptic segmentation; Hyperparameter Optimization; Deep learning; Automated Machine Learning; LiDAR-uppfattning; 4D-panoptisk segmentering; hyperparameteroptimering; djupinlärning; automatiserad maskininlärning;

    Sammanfattning : Environment perception is crucial to autonomous driving. Panoptic segmentation and objects tracking are two challenging tasks, and the combination of both, namely 4D panoptic segmentation draws researchers’ attention recently. LÄS MER

  2. 2. A Deep Learning Based Approach to Object Recognition from LiDAR Data Along Swedish Railroads

    Master-uppsats, KTH/Fastigheter och byggande

    Författare :Egil Morast; [2022]
    Nyckelord :Deep learning; DGCNN; LiDAR; Object recognition; Railroad; Automatisation; Sweden; Point cloud; Djupinlärning; DGCNN; LiDAR; Objektigenkänning; Järnväg; Automatisering; Sverige; Punktmoln;

    Sammanfattning : Malfunction in the overhead contact line system is a common cause of disturbances in the train traffic in Sweden. Due to the preventive methods being inefficient, the Swedish Transport Administration has stated the need to develop the railroad maintenance services and has identified Artificial Intelligence (AI) as an important tool for this undertaking. LÄS MER

  3. 3. LiDAR Point Cloud De-noising for Adverse Weather

    Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Johan Bergius; Jesper Holmblad; [2022]
    Nyckelord :Semantic Segmentation; Lidar point cloud; CNN; GAN; CycleGAN; Unsupervised; LiOR; DSOR; DROR; WADS;

    Sammanfattning : Light Detection And Ranging (LiDAR) is a hot topic today primarily because of its vast importance within autonomous vehicles. LiDAR sensors are capable of capturing and identifying objects in the 3D environment. However, a drawback of LiDAR is that they perform poorly under adverse weather conditions. LÄS MER

  4. 4. Segmentation, Classification and Tracking of Objects in LiDAR Point Cloud Data Using Deep Learning

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Hjalmar Lind; Robin Holtz Bernståle; [2022]
    Nyckelord :Deep Learning; Point Cloud; Segmentation; Classification; Tracking; LiDAR; PointNet; Technology and Engineering;

    Sammanfattning : The purpose of this thesis was to explore deep learning methods of segmentation, classification and tracking of objects in LiDAR data. To do this a complete pipeline was developed, consisting of background filtering, clustering, tracking, labeling and visualization. LÄS MER

  5. 5. Improving a Background Model for Tracking and Classification of Objects in LiDAR 3D Point Clouds

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Seamus Doyle; Gustav Nilsson; [2022]
    Nyckelord :LiDAR; Semantic Segmentation; Neural Networks; RandLA-NET; 3D Point Cloud; Gaussian Process Regression; Robust Locally Weighted Regression; CARLA; Ground Model; Technology and Engineering;

    Sammanfattning : This thesis studied methods of improving a background model for a data processing pipeline of LiDAR point clouds. For this, two main approaches were evaluated. The first was to implement and compare three different models for detecting ground in a point cloud. These were based on more classical modeling approaches. LÄS MER