{"url":"/dataset/inlut3d","name":"InLUT3D","full_name":"Indoor Lodz University of Technology Point Cloud Dataset","description_markdown":"This dataset called Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) is a point cloud set tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.\r\n\r\nThe points are divided into 18 distinct categories outlined in the `label.yaml` file along with their respective codes and colors. Among categories you will find:\r\n\r\n-    ceiling,\r\n-    floor,\r\n-    wall,\r\n-    stairs,\r\n-    column,\r\n-    chair,\r\n-    sofa,\r\n-    table,\r\n-    storage,\r\n-    door,\r\n-    window,\r\n-    plant,\r\n-    dish,\r\n-    wallmounted,\r\n-    device,\r\n-    radiator,\r\n-    lighting,\r\n-    other.\r\n\r\nSeveral challenges are intrinsic to the presented dataset:\r\n\r\n1.    Extremely non-uniform categories distribution across the dataset.\r\n1.    Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.\r\n1.    Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).\r\n1.    High point density throughout the dataset.\r\n\r\nEach PTS file contains 8 columns:\r\n\r\n| **Column ID** | **Description**                                 |\r\n|---------------|-------------------------------------------------|\r\n| 1             | X Cartesian coordinate                          |\r\n| 2             | Y Cartesian coordinate                          |\r\n| 3             | Z Cartesian Coordinate                          |\r\n| 4             | Red colour in RGB space in the range [0, 255]   |\r\n| 5             | Green colour in RGB space in the range [0, 255] |\r\n| 6             | Blue colour in RGB space in the range [0, 255]  |\r\n| 7             | Category code                                   |\r\n| 8             | Instance ID                                     |","description_withheld":null,"homepage":"https://zenodo.org/records/8131487","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution Non Commercial 4.0 International","url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode.en"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","datasets_with_task":"/datasets/task/3d-instance-segmentation-1"},{"name":"3D Classification","url":"/task/3d-classification","datasets_with_task":"/datasets/task/3d-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["InLUT3D"],"data_loaders":[{"repo":"https://github.com/jamesWalczak/pytorch_geometric","url":"https://github.com/jamesWalczak/pytorch_geometric","frameworks":["pytorch"]}],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}