How to Generate Synthetic 3D Point Cloud Rooms with Labels (Python Tutorial)

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This tutorial demonstrates how to generate unlimited synthetic 3D point cloud rooms with perfect semantic labels using simple Python code. It covers creating basic 3D shapes, assembling complete room environments, adding realistic noise patterns, assigning semantic labels automatically, visualizing results with color-coding, and exporting to industry-standard formats. The approach is useful for training segmentation algorithms, testing robotics systems, and developing computer vision applications. Timestamps: [00:00] The problem with real-world 3D data [02:05] The Toolbox [02:57] The Workflow [04:46] Creating basic geometric shapes [11:10] Assembling complete room environments [12:05] Adding semantic labels automatically [12:54] Exporting to industry-standard PLY format [14:12] Visualizing results with Open3D [15:45] Using synthetic data for ML training [18:05] 3D Visualization with Software [19:45] Download the complete code