Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1610019(2021)
Scattered Point Cloud Simplification Algorithm Based on Adaptive Neighborhood and Local Contribution Value
Fig. 2. Surface distribution of three datasets. (a) Average curvature; (b) Gaussian curvature
Fig. 3. Bunny model and reconstruction results. (a) Original point cloud; (b) original point cloud reconstruction; (c) simplification result of the proposed algorithm with 90% simplification rate; (d) reconstruction of simplification result of the proposed algorithm with 90% simplification rate
Fig. 4. Comparison on the Bunny model. (a) Standard deviation of error; (b) maximum grid area; (c) difference in reconstruction area
Fig. 5. Armadillo model and reconstruction results. (a) Original point cloud; (b) original point cloud reconstruction; (c) simplification result of the proposed algorithm with 90% simplification rate; (d) reconstruction of simplification result of the proposed algorithm with 90% simplification rate
Fig. 6. Comparison on the Armadillo model . (a) Standard deviation of error; (b) maximum grid area; (c) difference in reconstruction area
Fig. 7. Chair model and reconstruction results. (a) Original point cloud; (b) original point cloud reconstruction; (c) simplification result of the proposed algorithm with 90% simplification rate; (d) reconstruction of simplification result of the proposed algorithm with 90% simplification rate
Fig. 8. Comparison on the Chair model. (a) Maximum grid area; (b) difference in reconstruction area
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Rudan Zheng, Jinlong Li, Yu Zhang, Xiaorong Gao. Scattered Point Cloud Simplification Algorithm Based on Adaptive Neighborhood and Local Contribution Value[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610019
Category: Image Processing
Received: Aug. 24, 2020
Accepted: Sep. 30, 2020
Published Online: Aug. 16, 2021
The Author Email: Jinlong Li (jinlong_lee@126.com)