Essays about: "3D-punktmoln"

Showing result 1 - 5 of 12 essays containing the word 3D-punktmoln.

1. 1. Dynamic Object Removal for Point Cloud Map Creation in Autonomous Driving : Enhancing Map Accuracy via Two-Stage Offline Model

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

   Author : Weikai Zhou; [2023]
   Keywords : Autonomous driving; Dynamic object removal; Map creation; 3D point cloud; Autonom körning; Dynamiska objekt borttagning; Skapande av kartor; 3D-punktmoln;

   Abstract : Autonomous driving is an emerging area that has been receiving an increasing amount of interest from different companies and researchers. 3D point cloud map is a significant foundation of autonomous driving as it provides essential information for localization and environment perception. READ MORE

3. 2. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation

   University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

   Author : Shams Methnani; [2023]
   Keywords : ;

   Abstract : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. READ MORE

4. 3. Deep Learning Semantic Segmentation of 3D Point Cloud Data from a Photon Counting LiDAR

   University essay from Linköpings universitet/Datorseende

   Author : Caspian Süsskind; [2022]
   Keywords : Deep Learning; Machine Learning; Computer vision; Semantic Segmentation; Photon Counting LiDAR; LiDAR; Point Cloud; 3D Data; Point Cloud Segmentation; Point Classification; Convolutional Neural Network; CNN; SPVCNN; Djupinlärning; LiDAR; fotonräknande LiDAR; semantisk segmentering; datorseende; punktmoln; maskininlärning;

   Abstract : Deep learning has shown to be successful on the task of semantic segmentation of three-dimensional (3D) point clouds, which has many interesting use cases in areas such as autonomous driving and defense applications. A common type of sensor used for collecting 3D point cloud data is Light Detection and Ranging (LiDAR) sensors. READ MORE

5. 4. An investigation of detecting potholes with UAV LiDAR and UAV Photogrammetry

   University essay from Högskolan i Gävle/Samhällsbyggnad

   Author : Linus Hedenström; Sebastian Eriksson; [2021]
   Keywords : UAV; LiDAR; photogrammetry; potholes; point clouds; SfM; paved; pavement; road; drone; UAV; LiDAR; fotogrammetri; potthål; drönare; asfalt; väg; punktmoln; SfM;

   Abstract : Potholes are caused by erosion and as such always emerging on our roadnetwork. Potholes may not only cause great damages to vehicles, but can alsocause road accidents, which in the worst case are fatal. READ MORE

6. 5. 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