三維場景重構能應用於多領域做分析、展示。本研究運用電腦視覺相關演算法，如運動回復結構 (Structure from Motion, SfM)、群集觀點多視立體 (Clustering Views for Multi-View Stereo, CMVS)、以區塊為基礎之多視立體 (Patch-Based Multi-View Stereo, PMVS)、柏松表面重構 (Poisson surface reconstruction, PSR)，透過多張影像重構三維場景模型。本研究使用消費型相機與智慧型裝置拍攝不同物體角度之影像，不需要額外提供攝影裝備的內外參數，即能重構室內外各種尺度的模型，物件由小到大分別為安全帽、水泥方柱、室外涼亭，以及 UAV空拍中興大學，並評估各模型的焦距精度。最後，重構三種不同的水泥方柱模型，利用圖標長度來還原模型真實尺度並進行體積評估。

3D scene reconstruction has been applied to broad fields to analyze complex 3D information and display 3D models. The study applies computer vision algorithms, including Structure from Motion (SfM), Clustering Views for Multi-View Stereo (CMVS), Patch-based Multi-View Stereo (PMVS), and Poisson Surface Reconstruction (PSR) to generate 3D scene models with numerous images. This study employs images taken by commercial cameras and smart devices for 3D scene reconstruction through aforesaid algorithms without the need of internal and external parameters. Also, 3D models can be reconstructed in different scales, such as both indoor and outdoor for example. There are four experimental objects containing the helmet and the concrete pillar as small scale models, the pavilion outdoor as a middle scale model, and National Chung Hsing University’s campus as a large scale model to estimate accuracy of focal lengths for 3D models respectively. Finally, reconstructing three different types of concrete pillars and applying constraint rules of a pattern scale was executed to recover the real scale models for volume estimation.