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Showing result 1 - 5 of 32 essays matching the above criteria.

1. 1. 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

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

   University essay from Lunds universitet/Matematik LTH

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

   Abstract : 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. READ MORE

4. 3. ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand

   University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Author : Gabriela Olekszyk; [2022]
   Keywords : Geography; GIS; Lidar; ALS; Point cloud; Accuracy; Quality; Technology and Engineering;

   Abstract : Gabriela Olekszyk ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand. Lidar (Light Detection and Ranging) data is becoming more widely available and accessible. READ MORE

5. 4. 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

6. 5. Identifying Piggybacking with Radar and Neural Networks

   University essay from Lunds universitet/Matematik LTH

   Author : Joel Sigurdsson; Hannes Olsson; [2022]
   Keywords : neural networks; classification; radar; access control; artificial; recurrent; convolutional; point clouds; cnn; ann; lstm; rnn; optimization; gradient descent; camera; piggybacking; tailgating; fmcw; pointnet; Mathematics and Statistics;

   Abstract : A common problem in access control is piggybacking. This is when a person without authorized access sneaks closely behind another with access through a door. This thesis seeks to answer whether using radar is a viable solution when attempting to detect piggybacking. READ MORE