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|Title:||3D point cloud analytics||Authors:||Png, Samuel Yao Wei||Keywords:||Engineering::Computer science and engineering||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Png, S. Y. W. (2022). 3D point cloud analytics. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162913||Project:||SCSE21-0660||Abstract:||As collection of real world data is tedious and can sometimes be difficult due to places being inaccessible by scanners, the study of incorporating synthetic data together with real world data will be conducted to find a potential solution. The purpose of this report investigates the act of performing semantic segmentation on 3D point clouds and explores the differences between using real world and synthetic dataset for training. SPVCNN based semantic segmentation model will be used in this study as a baseline and is selected due to its good performance as compared to other architectures. Self-training for unsupervised domain adaptation used commonly in 2D semantic segmentation will be studied and attempted on 3D points clouds to bridge the differences between the source and the target domain . Results from the baseline model, compared and analysed with the self-training model shows improvement in prediction accuracy but mostly only for one of the dominant classes. A deeper look into the model predictions explains the reasons for such improvement and proposes potential solutions for enhancing the model even further.||URI:||https://hdl.handle.net/10356/162913||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on Nov 30, 2022
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