Core Insights - The company, Ruis, announced the official opening of 10,000 indoor 3D datasets named Realsee3D for academic research and non-commercial use, marking it as potentially the largest spatial 3D dataset globally, aimed at providing high-quality data for researchers and developers in the spatial intelligence field [1] Group 1: Dataset Features - Realsee3D is a large-scale multi-view RGB-D dataset designed to advance research in indoor 3D perception, reconstruction, and scene understanding [5] - The dataset includes 10,000 unique indoor 3D scenes, 95,962 segmented room units, and 299,073 pairs of RGB-D images [6] - It features comprehensive annotations for multi-task learning, extending beyond visual data to include geometric and semantic information [5][6] Group 2: Data Collection and Composition - The dataset consists of 1,000 real scenes capturing complex lighting, layouts, and living traces from the physical world, alongside 9,000 synthetic scenes based on over 100 professionally designed style templates [6] - It provides various data types, including color panoramic images, depth maps, CAD drawings, floor plans, semantic segmentation labels, and 3D object detection labels [6][8][10][12] Group 3: Industry Impact and Accessibility - The Realsee3D dataset addresses a significant gap in high-quality spatial data that has long hindered research and applications in the spatial intelligence field [14] - The dataset is available for global researchers and developers to download through the official Ruis GitHub repository, encouraging collaboration in exploring the future boundaries of spatial intelligence research [14]