ShotcreteDepth：一個用於噴射混凝土施工環境中穩健機器人深度感知的雙模態數據集

摘要

我們提出ShotcreteDepth，這是一個來自建築領域的雙模態數據集，同時捕捉了主動噴射混凝土過程及一般建築環境。該數據集包含在嚴峻現實條件下（如高濁度與低光照）獲取的立體RGB影像與LiDAR點雲。此類條件會對感測器量測造成不利影響，導致不完整且具雜訊的觀測結果，對自動化應用中的感知系統構成重大挑戰。除數據集外，我們還釋出了一套輕量級標註工具，專為高效標註LiDAR點雲而設計。ShotcreteDepth由11,252個時間同步的數據樣本組成，其中220個已為評估目的進行標註。該數據集支持在密切反映工業環境操作複雜度的條件下，進行立體匹配、深度補全及深度估計等研究。專案倉儲：https://github.com/dtu-pas/shotcrete-depth

English

We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprises stereo RGB imagery and LiDAR point clouds acquired under harsh real-world conditions, including high turbidity and poor illumination. Such conditions adversely affect sensor measurements, leading to incomplete and noisy observations that pose significant challenges for perception systems in autonomous applications. Alongside the dataset, we release a lightweight annotation tool designed for time-efficient labeling of LiDAR point clouds. ShotcreteDepth consists of 11,252 temporally synchronized data samples, of which 220 are annotated for evaluation purposes. The dataset supports research in stereo matching, depth completion, and depth estimation under conditions that closely reflect the operational complexities found in industrial settings. Project repository: https://github.com/dtu-pas/shotcrete-depth