Essays about: "3D-Point Clouds"
Showing result 1 - 5 of 32 essays containing the words 3D-Point Clouds.
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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 statistikAbstract : 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
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2. Improving a Background Model for Tracking and Classification of Objects in LiDAR 3D Point Clouds
University essay from Lunds universitet/Matematik LTHAbstract : 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
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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 ekosystemvetenskapAbstract : 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
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4. Deep Learning Semantic Segmentation of 3D Point Cloud Data from a Photon Counting LiDAR
University essay from Linköpings universitet/DatorseendeAbstract : 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
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5. Identifying Piggybacking with Radar and Neural Networks
University essay from Lunds universitet/Matematik LTHAbstract : 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