建議

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立 政 治 大 學

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N a tio na

l C h engchi U ni ve rs it y

第二節 建議

(1) 本研究使用較常見的影像匹配演算法，如 SIFT、SURF 及 ASIFT 演算 法等，因每張全景影像拍攝視角變化較大，即便是擁有仿射不變特性 的ASIFT 演算法仍僅能獲得相對多數的匹配點。建議可針對全景影像 發展匹配方法，使其更適合用於全景影像。

(2) 因大部分的室內場景是矩形的空間，研究測試拍攝方式能適用於正、

長方形場景，但室內仍包含長走廊、挑高大廳、體育館等等特殊場景，

建議未來可測試拍攝方式是否適用於特殊場景之中。

(3) 為使初始值計算方便，本研究將相機保持水平拍攝，但會造成匹配點 集中在特徵較多的牆面，即影像中間處，影像上、下方為天花板及地板 之區域，匹配點較為缺乏，使得在像點給予不同先驗精度的實驗中，結 果僅提升毫米等級。建議未來可在布滿率定標的房間預先執行像點變 形改正，在連結點分布均勻時，計算各網格中心的系統誤差改正值，並 可測試率定結果應用於後續球形全景影像定位定向像點系統差預改正 的合適性，以及再測試像點是否需依位置給予不同先驗精度。

(4) 目前建置室內模型時，是以人工方式量測角點的影像坐標進行前方交 會，建議未來可發展適用於全景影像之密匹配技術，產製三維點雲資 料，加快模型建置的速度及細緻程度。

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