Sökning: "djupuppskattning"

Hittade 5 uppsatser innehållade ordet djupuppskattning.

1. 1. Deep Learning-Based Depth Estimation Models with Monocular SLAM : Impacts of Pure Rotational Movements on Scale Drift and Robustness

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Daniel Bladh; [2023]
   Nyckelord :Deep Learning; Computer Vision; Monocular; SLAM; Depth Estimation;

   Sammanfattning : This thesis explores the integration of deep learning-based depth estimation models with the ORB-SLAM3 framework to address challenges in monocular Simultaneous Localization and Mapping (SLAM), particularly focusing on pure rotational movements. The study investigates the viability of using pre-trained generic depth estimation networks, and hybrid combinations of these networks, to replace traditional depth sensors and improve scale accuracy in SLAM systems. LÄS MER

3. 2. Mobile-based 3D modeling : An indepth evaluation for the application to maintenance and supervision

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

   Författare :Martin De Pellegrini; [2021]
   Nyckelord :Computer Vision; 3D Reconstruction; Deep Learning; Indoor; Digital Twin; Point Cloud.; Datorsyn; 3Drekonstruktion; Deep Learning; inomhus; Digital Twin; Point Cloud.;

   Sammanfattning : Indoor environment modeling has become a relevant topic in several applications fields including Augmented, Virtual and Mixed Reality. Furthermore, with the Digital Transformation, many industries have moved toward this technology trying to generate detailed models of an environment allowing the viewers to navigate through it or mapping surfaces to insert virtual elements in a real scene. LÄS MER

4. 3. Depth Estimation from Images using Dense Camera-Lidar Correspondences and Deep Learning

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

   Författare :Ajinkya Khoche; [2020]
   Nyckelord :;

   Sammanfattning : Depth estimation from 2D images is a fundamental problem in Computer Vision, and is increasingly becoming an important topic for Autonomous Driving. A lot of research is driven by innovations in Convolutional Neural Networks, which efficiently encode low as well as high level image features and are able to fuse them to find accurate pixel correspondences and learn the scale of the objects. LÄS MER

5. 4. Road Surface Preview Estimation Using a Monocular Camera

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Marcus Ekström; [2018]
   Nyckelord :Road Surface Preview; Computer Vision; Depth Estimation; Convolutional Neural Network; CNN; traffic safety; monocular camera; mono vision system; mono camera; Structure from motion; sfm; 3D Reconstruction; Autonomous Driving; Datorseende; trafiksäkerhet; djupuppskattning; mono kamera; 3D rekonstruktion; autonoma fordon;

   Sammanfattning : Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. LÄS MER

7. 5. Segmentation and Depth Estimation of Urban Road Using Monocular Camera and Convolutional Neural Networks

   Master-uppsats, KTH/Robotik, perception och lärande, RPL

   Författare :Addi Djikic; [2018]
   Nyckelord :AI; ANN; CNN; semantic; segmentation; autonomous; Scania; driving; road; pixel; classification; regression; real time; monocular; depth; estimation; convolutional; neural; networks; deep; learning; perception; camera; vehicles; supervised; tensorflow; Cityscapes; machine learning; autoencoder; decoder; encoder;

   Sammanfattning : Deep learning for safe autonomous transport is rapidly emerging. Fast and robust perception for autonomous vehicles will be crucial for future navigation in urban areas with high traffic and human interplay. Previous work focuses on extracting full image depth maps, or finding specific road features such as lanes. LÄS MER