本文爲代爾夫特理工大學（作者：Teng Wu）的碩士論文，共119頁。
本文提出了一種新的基於中軸變換（MAT）的點雲可見性分析方法。這種基於MAT的方法具有幾個優點，該方法避免了點雲曲面重建，適用於輸入點雲中缺少曲面的情況。對於網格生成的點雲、AHN3點雲和稠密圖像匹配生成的點雲等不同的點雲數據集，該方法都能成功地爲它們提供良好的可見性結果。
本文所要克服的主要挑戰是內外MAT分離。本文對兩種方法，即正態重定位法和基於平分線的方法進行了實驗研究。法向重定位方法僅適用於由網格生成的點雲。基於平分線的方法適用於所有的數據集測試。當曲面缺失時，該方法能夠成功地將內外MAT分開。爲了加快查詢速度，生成了內部MAT的空間索引。本文實現了KD樹和R樹兩種空間索引。由於KD樹實現的侷限性，KD樹並沒有明顯提高運行速度。KD樹的實現還有待改進。R樹大大提高了查詢的運行時間。對於51567個點，基於R樹的查詢在1880ms內完成。總之，本文提出了一種有效的基於MAT的點雲可見性分析方法。
This thesis proposes a novel medial axistransform (MAT) based method to achieve visibility analysis in a point cloud.There are several advantages of this MAT based method. This method avoidssurface reconstruction from a point cloud. It also works for the situation whenthere is surface missing in the input point cloud. For different point clouddatasets such as point cloud generated from meshes, AHN3 point cloud and pointcloud generated from dense image matching, this method successfully deliverdecent visibility result for all of them. The main challenge overcome in thisthesis is the interior and exterior MAT separation. Two approaches, normalreorientation approach and bisector based approach are experimented in thethesis to separate MAT. The normal reorientation approach only works for pointcloud generated from meshes. The bisector based approaches works for all thedatasets testes. It successfully separates the interior and exterior MAT whenthere is surfacing missing. To speed up of the query process, spatial index isgenerated for interior MAT. In this thesis, two spatial indices areimplemented, KD-Tree and R-Tree. Due to the limitation of my KD-Treeimplementation, the KD-Tree does not improve the running speed obvious. Thereis a room to improve the KD-Tree implementation. The R-Tree achieves sharplyimprovement on running time of the queries. For 51567 points, the query basedon R-Tree finished in 1880 ms. In a word, this thesis proposed an efficient MATbased method of visibility analysis in a point cloud.
- 引言
- 理論
- 論文相關工作
- 研究方法
- 具體實現與實驗
- 結果與討論
- 結論與未來工作展望
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