以人工紋理協助弱紋理區影像三維重建

Abstract

隨著電腦設備及攝影測量技術的進步，立體匹配已可有效產製高密度點雲。但是，對於弱紋理區，考量場景或物件地域性、施作條件及硬體需求性，在人工紋理的輔助作業模式下，還有許多限制及作業挑戰待解決。立體匹配藉由投射人工紋理於場景或物件以增加其紋理變化及豐富度，考量場景幾何、大小及所需重建之完整度，會對應不同的方法選擇、設備需求及操作細節。本研究採用一般消費型投影機及相機，在低成本的設備需求下搭配所設計人工紋理、影像拍攝配置、像點量測與方位解算一套流程拍攝影像及獲致品質足夠的方位參數，並利用SURE進行影像密匹配獲取三維點雲。本研究工作具體貢獻主要分為人工紋理設計及取像幾何規劃，人工紋理包括用於影像密匹配的匹配紋理及連接拍攝不同紋理的相鄰像對的連結紋理；由所需的重建精度，可推求攝影測量作業參數包含像主距、物距、像基距及f-number等參數，結合適當的相機與投影機之設備配置以進行影像獲取。本研究從模擬場景的三維重建進行模擬資料與實際資料成果分析，再推展至真實場景之三維重建。本研究提出的人工紋理及設備配置對人工場景及真實場景均可達至預期精度中的三維重建成效。

Stereo matching can effectively produce dense point cloud, but it may encounter great challenge when faced with low-textured image content. Even added with artificial texture, the location of the targeted scene, operation condition, and hardware requirement, among others, still demand considerable concerns to arrange for appropriate work scheme. This work captures images by low cost projectors and a camera, and dense matching software SURE is used to generate 3D point clouds. Regarding how and what to project textures, the main focus is on analyzing and designing suitable textures taking the surface, geometry, and making succession of scene into consideration. On the other hand, the fine placements of projectors and camera stations are also crucial to maintaining quality imaging geometry. The four main parameters for image acquisition, baseline, object distance, principal distance, and f-number can be determined by the required accuracy for 3D reconstruction. This study establishes appropriate work steps and rules for reconstructing low texture scene, and the experiments validate its effectiveness and applicability meeting quality requirement.