建築資訊模型(Building Information Modeling, BIM)在建築生命週期(Building Lifecycle Management, BLM)的應用上展現了的價值，然而，實務上，BIM模型經常因為設計變更或整合困難，導致BIM模型常有與現地狀況不符或產生錯誤的情況發生。如何使建築生命週期各階段之BIM模型與實際場景一致，目前仍然缺乏一個標準化的處理機制，為此，本研究建立BIM vs BIM (BvB)與Scan vs BIM (SvB)兩種檢測任務之作業流程，提出能針對不同類別的構件進行變異識別之演算法，以輔助各階段中的BIM模型更新。為完備擬議之框架，本研究採用移動式平台搭載深度相機蒐集現地點雲資料，以及實現模型產製點雲(BIM-to-Point clouds)策略，產出無誤差BIM構件點雲及繼承構件之語意資訊，並且提出變異構件識別演算法(Changed Component Identification, CCI)，識別兩數據集中所有構件之狀態類別，包括存在、移動、新增、缺失及待定物件五種類別，同時記錄變異構件之語意標籤及位置。最後，研究使用現地點雲與時變BIM模型針對整體框架進行測試，其中BvB與SvB任務中的評估指標分別達到約93%與80%的準確率，顯示良好的預測成果。研究中除了驗證研擬框架的可行性，也針對CCI演算法的相似度檢查進行深入探討，測試不同搜尋半徑、點密度及點雲品質等因素對於相似度檢查上之影響，提供演算法在實務應用上的參數設置依據。研究成果能減少BIM模型在更新作業上所耗費的人力及時間成本，進而推動BIM於建築生命週期上的應用與發展。

This study presents an automatic framework for both Scan-vs-BIM and BIM-vs-BIM model renewal. To this end, a hybrid simultaneous localization and mapping (SLAM), a BIM-to-point cloud strategy, and object-based changed component identification (CCI) are proposed to complete the framework. The hybrid SLAM enhances robustness to the limitations of indoor environments while the CCI cooperating with the BIM-to-point cloud strategy can identify five changing states along with locations and semantic information of the changed components. Besides, experiments with different variables were conducted to gain insight into the effectiveness of dealing with various point cloud quality and explore the similarity level of corresponding components under different point densities and quality for the reference of parameter configuration. Finally, validations of the BIM-vs-BIM and Scan-vs-BIM cases achieved an accuracy of 93% and 80%, respectively. The framework is expected to improve the automated level of practical BIM model renewal.

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