计算机辅助设计与图形学学报
計算機輔助設計與圖形學學報
계산궤보조설계여도형학학보
JOURNAL OF COMPUTER-AIDED DESIGN & COMPUTER GRAPHICS
2015年
4期
614-620
,共7页
王醒策%蔡建平%武仲科%周明全
王醒策%蔡建平%武仲科%週明全
왕성책%채건평%무중과%주명전
法向估计%法向重定向%主元分析%点邻域%移动最小二乘曲面
法嚮估計%法嚮重定嚮%主元分析%點鄰域%移動最小二乘麯麵
법향고계%법향중정향%주원분석%점린역%이동최소이승곡면
normal estimation%normal orientation%principal component analysis%neighborhood size%moving least squares surface
为了提高对点云模型处理的有效性,提出一种对点云模型的法向估计和重定向方法。首先利用基于局部平面拟合的主元分析方法得到初步法向估计;然后改进移动最小二乘曲面实现局部曲面拟合,进一步得到更加准确的法向,实现了点云模型的去噪光顺;最后通过增加切向约束规则来修正法向重定向中的法向传播方向。实验结果表明,对于具有复杂细节(如紧邻面、尖角形状等)的点云模型,该方法可以提高法向计算的准确度,并得到光顺的点云模型。在实际应用中,该方法可以很好地应用于点云模型的预处理,为后续的模型处理和分析提供良好的数据基础。
為瞭提高對點雲模型處理的有效性,提齣一種對點雲模型的法嚮估計和重定嚮方法。首先利用基于跼部平麵擬閤的主元分析方法得到初步法嚮估計;然後改進移動最小二乘麯麵實現跼部麯麵擬閤,進一步得到更加準確的法嚮,實現瞭點雲模型的去譟光順;最後通過增加切嚮約束規則來脩正法嚮重定嚮中的法嚮傳播方嚮。實驗結果錶明,對于具有複雜細節(如緊鄰麵、尖角形狀等)的點雲模型,該方法可以提高法嚮計算的準確度,併得到光順的點雲模型。在實際應用中,該方法可以很好地應用于點雲模型的預處理,為後續的模型處理和分析提供良好的數據基礎。
위료제고대점운모형처리적유효성,제출일충대점운모형적법향고계화중정향방법。수선이용기우국부평면의합적주원분석방법득도초보법향고계;연후개진이동최소이승곡면실현국부곡면의합,진일보득도경가준학적법향,실현료점운모형적거조광순;최후통과증가절향약속규칙래수정법향중정향중적법향전파방향。실험결과표명,대우구유복잡세절(여긴린면、첨각형상등)적점운모형,해방법가이제고법향계산적준학도,병득도광순적점운모형。재실제응용중,해방법가이흔호지응용우점운모형적예처리,위후속적모형처리화분석제공량호적수거기출。
The accuracy of normal estimation and normal orientation have great impacts on the processing of point cloud models, such as denoising, registering and surface reconstruction. In terms of normal estimation for 3D point cloud models, firstly the local plane approximation based on PCA method was used to get a preliminary normal estimation. And then the improved Moving Least Squares Surface (MLSS) method was used to get local approximate surface, and thus produce more accurate normals which were resilient to noises. For normal orienta-tion, a new rule of tangential constraint was proposed in the process of normal propagation. Finally, the Poisson surface reconstruction approach was employed to verify the effectiveness of estimated normals. Experimental re-sults show that the accuracy of normal estimation is improved by our method, and smooth models can be obtained as well. Our proposed method can be very useful in the preprocessing of point cloud models.