3D Semantic Segmentation

We complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. This produces the first large-scale, real-world 3D dataset with dense semantic annotations. Training on this auto-generated data, we push forward the state-of-the-art performance on ScanNet and ScanNet200 with prevalent 3D semantic segmentation models.

ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10km2 with close to 600 million points and features eleven distinct object categories. To guarantee the dataset's quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3D urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications. ## Annotations: * Ground (2) : All points representing the Earth’s surface, including, soil, pavement, roads, and the bottom of water bodies. * Vegetation (3) : All points representing organic plant life, ranging from trees, low shrubs, and tall grass of all heights. * Buildings (4) : Man-made structures characterized by roofs and walls, encompassing houses, factories, and sheds. * Noise (5) : Sporadic points suspended in air or underground. * Transmission Wires (6) : High-voltage wires for long-distance transmission from power plants to substations. Either directly connected to transmission towers or poles. Also includes transmission ground wires. * Distribution Wires (7) : Lower-voltage overhead distribution wires distributing electricity from substation to end users. Includes span guy wires and communication wires. * Poles (8) : Utility poles used to support different types of wires or electroliers. These can include poles with either transmission or distribution wires. Down guy wires, crossarms and transformers are also included in this class. * Transmission Towers (9) : Large structures supporting transmission wires with the distinct characterisation of steel lattices and cross beams. * Fence (10) : Barriers, railing, or other upright structure, typically of wood or wire, enclosing an area of ground. * Vehicle (11) : All wheeled vehicles that can be driven. * Unassigned (1) : This category serves as a catch-all for non-subject points. Anything that is not on the class list is classified as Unassigned. These include wooden pallets, trash, structures not large or strong enough to put under buildings (tents, boulders, etc.), and house antennas.

Stack of 2D gray images of glass fiber-reinforced polyamide 66 (GF-PA66) 3D X-ray Computed Tomography (XCT) specimen. Usage: 2D/3D image segmentation Format: HDF5 Libraries to read HDF5 files: 1) silx: https://github.com/silx-kit/silx 2) h5py: https://www.h5py.org 3) pymicro: https://github.com/heprom/pymicro Trained models to segment this dataset: https://doi.org/10.5281/zenodo.4601560 Please cite us as @ARTICLE{10.3389/fmats.2021.761229, AUTHOR={Bertoldo, João P. C. and Decencière, Etienne and Ryckelynck, David and Proudhon, Henry}, TITLE={A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materials}, JOURNAL={Frontiers in Materials}, VOLUME={8}, YEAR={2021}, URL={https://www.frontiersin.org/article/10.3389/fmats.2021.761229}, DOI={10.3389/fmats.2021.761229}, ISSN={2296-8016}, }

Stack of 2D gray images of glass fiber-reinforced polyamide 66 (GF-PA66) 3D X-ray Computed Tomography (XCT) specimen. Usage: 2D/3D image segmentation Format: HDF5 Libraries to read HDF5 files: 1) silx: https://github.com/silx-kit/silx 2) h5py: https://www.h5py.org 3) pymicro: https://github.com/heprom/pymicro Trained models to segment this dataset: https://doi.org/10.5281/zenodo.4601560 Please cite us as @ARTICLE{10.3389/fmats.2021.761229, AUTHOR={Bertoldo, João P. C. and Decencière, Etienne and Ryckelynck, David and Proudhon, Henry}, TITLE={A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materials}, JOURNAL={Frontiers in Materials}, VOLUME={8}, YEAR={2021}, URL={https://www.frontiersin.org/article/10.3389/fmats.2021.761229}, DOI={10.3389/fmats.2021.761229}, ISSN={2296-8016}, }

Heritage Pointcloud Instance Collection dataset, acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models. Devid Campagnolo, Elena Camuffo, Umberto Michieli, Paolo Borin, Simone Milani and Andrea Giordano, "Fully Automated Scan-to-BIM via Point Cloud Instance Segmentation", In Proceedings of the International Conference on Image Processing (ICIP) 2023.

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. The 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: - ceiling, - floor, - wall, - stairs, - column, - chair, - sofa, - table, - storage, - door, - window, - plant, - dish, - wallmounted, - device, - radiator, - lighting, - other. Several challenges are intrinsic to the presented dataset: 1. Extremely non-uniform categories distribution across the dataset. 1. Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors. 1. Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam). 1. High point density throughout the dataset. Each PTS file contains 8 columns: | Column ID | Description | |---------------|-------------------------------------------------| | 1 | X Cartesian coordinate | | 2 | Y Cartesian coordinate | | 3 | Z Cartesian Coordinate | | 4 | Red colour in RGB space in the range [0, 255] | | 5 | Green colour in RGB space in the range [0, 255] | | 6 | Blue colour in RGB space in the range [0, 255] | | 7 | Category code | | 8 | Instance ID |