London Hall 3 Floors Point Cloud

This dataset contains a high-precision 3D point cloud captured from a furnished community hall in central London using professional LiDAR scanning equipment. The scan provides detailed spatial information of a multi-floor community hall environment including furniture, fixtures, and architectural elements.

Captured by professional measured surveyors, this dataset offers real-world commercial building complexity ideal for training computer vision models, autonomous robot navigation systems, and spatial AI applications. The point cloud includes survey-grade accuracy throughout the scanned area.

Perfect for companies developing indoor navigation algorithms, construction AI systems, digital twin applications, and robotics path planning solutions requiring authentic UK commercial building data.

• Point Coordinates (X,Y,Z): 3D spatial coordinates for each scanned point with millimeter precision • Intensity Values: LiDAR return intensity data for material classification and surface analysis

Data Volume: Approximately 15-50 million points depending on scan density settings File Formats: Available in .las, .e57, .xyz, and .ply formats File Size: 200MB - 2GB depending on format and compression Coverage Area: ~200-1,000 square meters of office space across multiple floors Point Density: 5-10mm point spacing typical

This dataset is ideal for a variety of applications:

Autonomous Robot Navigation: Training indoor navigation algorithms for commercial office environments, obstacle detection, and path planning systems.

Computer Vision Research: Developing 3D object recognition, spatial understanding, and scene segmentation models for built environments.

Construction AI & Digital Twins: Building information modeling automation, progress monitoring systems, and facility management applications.

Spatial AI Development: Training models for indoor positioning, room layout understanding, and architectural element detection.

Academic Research: Supporting research in robotics, computer vision, and spatial intelligence applications.

Proprietary

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