3D point cloud analysis
This paper presents a study that investigates the effectiveness of various sampling approaches when combined with the KPConv framework for 3D point cloud segmentation. The focus is mostly on the original grid subsampling strategy employed by the framework. In this study, a series of experiments w...
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| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172013 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper presents a study that investigates the effectiveness of various sampling approaches
when combined with the KPConv framework for 3D point cloud segmentation. The focus is
mostly on the original grid subsampling strategy employed by the framework. In this study, a
series of experiments were conducted to assess and contrast the outcomes derived from the
utilization of three distinct techniques: inherent grid subsampling, random sampling, and the
Farthest Point Sampling (FPS) approach. Initial results suggest that there are differences in the
accuracy and training efficiency of the model. The objective of this study is to provide a
thorough examination, elucidating the benefits and possible limitations of each approach. The
objective of this study is to provide valuable insights into the optimization of point cloud
processing techniques and to establish the superiority of a certain sampling approach in the
context of KPConv-based point cloud analysis. |
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