원문정보
초록
영어
In order to remove redundant data and resolve conflicts in point clouds, we proposed fuzzy clustering reduction strategy in this paper. Original point clouds are decreased before computing other pretreatments. The proposed method involves three processes: reduction of the original data using fuzzy clustering while the point clouds are divided into sub-domains using octree structure, generation of the sub-surface that is fitted the sub-surface by implicit function in each sub-domain, the normal alignment that are computed normal of sub-surface and inference the global normal of surface using iteratively propagate algorithm. The method is suitable to reduce mass point clouds to reconstruct surface that can keep the property of surface. The experimental results show that the model with less sharp feature is more effective than complex model to reduce point clouds by fuzzy clustering.
목차
1. Introduction
2. Related Work
3. Reduction Strategy of Point Clouds using Fuzzy c-mean Clustering
3.1. Reduce Point Clouds Framework during Partition of Unity
3.2. Parameters of the FCM Algorithm
4. Implicit Function Locally Fitting Framework
4.1. Implicit Model for Point Clouds
4.2. Stitch the Local Surface Framework by Advancing Front Algorithm
5. Results
6. Conclusions
Acknowledgment
References