查詢結果分析
來源資料
相關文獻
- 基於幾何特徵保留的自適應點雲簡化
- 臺灣上市公司財務評等中羅吉斯模式與判別分析模式之比較
- 序列相關資料的主成分分析法--以德基水庫水質監測數據為例
- 中國因應全球氣候變遷之政策與成效評估
- 輸電線故障偵測、辨識與定位演算法之軟硬體實現與模擬
- 陣列式多壁奈米碳管/高分子複合材料電阻感測器對化學氣體感測之研究
- 臺灣各縣市道路安全績效之比較研究
- Evaluating the Influence of PCDD/F Emission from Municipal Solid Waste Incinerators on Ambient Air by PCDD/F Concentration Isopleths and Principal Component Analysis
- 應用主成分分析法研究秈稻品種雜交親本之選擇
- 國內區域及地理學門專業期刊之排序
頁籤選單縮合
| 題 名 | 基於幾何特徵保留的自適應點雲簡化=Adaptive Point Cloud Simplification Based on Geometric Feature Preservation |
|---|---|
| 作 者 | 黃琳軒; 趙鍵哲; | 書刊名 | 航測及遙測學刊 |
| 卷 期 | 30:1 2025.03[民114.03] |
| 頁 次 | 頁23-46 |
| 分類號 | 440.91 |
| 關鍵詞 | 點雲簡化; 自適應鄰域; 特徵保留; 主成分分析法; Point cloud simplification; Adaptive neighborhood; Feature preservation; Principal component analysis; PCA; |
| 語 文 | 中文(Chinese) |
| DOI | 10.6574/JPRS.202503_30(1).0002 |
| 中文摘要 | 光學點雲應用於各領域,龐大數據影響存儲與運算效能,如何簡化點雲並保留幾何特徵成為關鍵。現有方法多依賴經驗法則,難適應不同區域特性。本研究改進了一種基於邊緣點、特徵點及非特徵點的點雲簡化方法,採用自適應鄰域大小保留特徵。方法上,先濾除雜訊,利用主成分分析法獲取曲率與熵建立拓樸結構,區分散亂與規則區域。於提取邊緣點後,計算特徵點重要性,對非特徵點減點,完成簡化與特徵保留。實驗顯示本方法在不同場景中能有效保留主要幾何特徵,然高簡化率下,細微特徵可能模糊。除此之外,未來需透過自動化參數數值選定提升效能。 |
| 英文摘要 | The application of photogrammetry point clouds across various fields often faces challenges due to the massive data volume, which affects storage and computational efficiency. Simplifying point clouds while preserving geometric features has thus become a critical task. Existing methods largely rely on empirical rules, making them less adaptable to varying regional characteristics. This study improves upon a point cloud simplification method based on edge points, feature points, and non-feature points, incorporating an adaptive neighborhood size to preserve features. The approach begins with noise filtering, followed by using Principal Component Analysis (PCA) to derive curvature and entropy for constructing a topological structure that distinguishes between irregular and regular regions. After extracting edge points, the importance of feature points is assessed, and non-feature points are reduced to achieve simplification with feature retention. Experimental results show that the proposed method effectively preserves major geometric features across different scenarios. However, at high simplification rates, fine details may become blurred. In the future, performance could be further enhanced by implementing automated parameter setup. |
本系統中英文摘要資訊取自各篇刊載內容。