Sökning: "3D feature extraction"
Visar resultat 1 - 5 av 22 uppsatser innehållade orden 3D feature extraction.
1. An evaluation study of 3D imaging technology as a tool to estimate body weight and growth in dairy heifers
Master-uppsats, SLU/Dept. of Animal Nutrition and ManagementSammanfattning : The aim of this thesis was to evaluate the use of a 3D camera as a tool to estimate body weight and growth in dairy heifers. Data collection lasted from October 2022 to January 2023 and was performed at the Swedish Livestock Research Centre in Uppsala, Sweden. 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. Feature-Aware Point Transformer for Point Cloud Alignment Classification : Pose your pose to FACT
Master-uppsats, Linköpings universitet/Institutionen för systemteknikSammanfattning : As the demand for 3D maps from LIDAR scanners increases, delivering high-quality maps becomes critical. One way to ensure the quality of such maps is through point cloud alignment classification, which aims to classify the alignment error between two registered point clouds. LÄS MER
4. Design, implementation and evaluation of a daylight estimation tool using 3D city model data
Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapSammanfattning : Solar energy is an important component of sustainable urban development. However, it is still not reaching its full potential due to several reasons. One of them is a lack of free tools based on open geodata capable of estimating solar energy (daylight) metrics on building features, e.g. LÄS MER
5. Robust Registration of ToF and RGB-D Camera Point Clouds
Master-uppsats, KTH/Fastigheter och byggandeSammanfattning : This thesis presents a comparison of M-estimator, BLAVE, and RANSAC method in point clouds registration. The comparison is performed empirically by applying all the estimators on a simulated data added with noise plus gross errors, ToF data and RGB-D data. The RANSAC method is the fastest and most robust estimator from the comparison. LÄS MER