ShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments
Summary
ShotcreteDepth is a bi-modal dataset of stereo RGB and LiDAR data from construction environments, designed to support research in depth perception under challenging conditions. The dataset includes 11,252 samples with 220 annotated, and is accompanied by a lightweight annotation tool.
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Paper page - ShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments
Source: https://huggingface.co/papers/2606.23152
Abstract
A bi-modal construction domain dataset combining stereo RGB and LiDAR data under challenging environmental conditions is introduced for autonomous system perception research.
We introduce ShotcreteDepth, abi-modal datasetfrom the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprisesstereo RGBimagery andLiDAR point cloudsacquired 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 ofLiDAR point clouds. ShotcreteDepth consists of 11,252 temporally synchronized data samples, of which 220 are annotated for evaluation purposes. The dataset supports research instereo matching,depth completion, anddepth estimationunder conditions that closely reflect the operational complexities found in industrial settings. Project repository: https://github.com/dtu-pas/shotcrete-depth
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