Development of techniques for compressing and updating 3D point cloud data

2021 Fiscal Year Final Research Report

• Project/Area Number: 18H01554
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 22050:Civil engineering plan and transportation engineering-related
• Research Institution: The University of Tokyo
• Principal Investigator: Fuse Takashi 東京大学, 大学院工学系研究科(工学部), 教授 (80361525)
• Project Period (FY): 2018-04-01 – 2021-03-31
• Keywords: 3次元点群 / レーザ計測 / 変化検出 / 圧縮 / 更新

Outline of Final Research Achievements

3D point cloud data acquisition has been rapidly progressive. The 3D point cloud data is expected to be widely utilized. Dealing with the 3D point cloud data is still challenging due to its massive data and updating strategy. This study developed a data compression method for 3D point cloud data obtained from MMS and aerial lasers scanner, and so on. The study also developed data updating methods, including change detection and geographic feature number/location estimation. The proposed framework was comprehensively evaluated from both compression and updating viewpoints. Accordingly, the proposed methodology for maintaining and updating essential data is shown.

Free Research Field

空間情報学

Academic Significance and Societal Importance of the Research Achievements

3次元点群データは、自動運転、防災・減災、インフラ維持管理などに重要なデータであるが、データ量やその後の処理に対する負荷も大きく、幅広い分野における利活用を阻害する一因にもなってきた。3次元点群データをデータ基盤と考えた場合、継続的な整備・利用の視点から、いかに更新するかの検討も重要になる。これらの課題を包括的に解決するために、状態空間モデル、スパースモデリング、深層学習の手法を統合・発展させ、新規性かつ創造性の高い方法論を構築した。