Sökning: "PointNet"
Visar resultat 1 - 5 av 11 uppsatser innehållade ordet PointNet.
1. Automating the identification of components in 3D models
Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknikSammanfattning : Online house building tools are robust instruments that have transformed the approach to home planning and design. The significance of 3D models on online house building and design platforms lies in their ability to elevate the user experience, enhance design precision, and foster collaboration. LÄS MER
2. Industrial 3D Anomaly Detection and Localization Using Unsupervised Machine Learning
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Detecting defects in industrially manufactured products is crucial to ensure their safety and quality. This process can be both expensive and error-prone if done manually, making automated solutions desirable. LÄS MER
3. Segmentation, Classification and Tracking of Objects in LiDAR Point Cloud Data Using Deep Learning
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : 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
4. Radar Detection Using Deep Learning
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : This thesis aims to reproduce and improve a paper about dynamic road user detection on 2D bird's-eye-view radar point cloud in the context of autonomous driving. We choose RadarScenes, a recent large public dataset, to train and test deep neural networks. LÄS MER
5. Pose classification of people using high resolution radar indoor
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Video cameras are the primary equipment used for indoor surveillance. There are however areas where alternatives are needed as the use of cameras is sensitive or forbidden, e.g. in homes, bathrooms or dressing rooms. LÄS MER