Abstract

S3DIS comprises 6 colored 3D point clouds from 6 large-scale indoor areas, along with semantic instance annotations for 12 object categories (wall, floor, ceiling, beam, column, window, door, sofa, desk, chair, bookcase, and board).

Methodology

The Stanford Large-Scale 3D Indoor Spaces (S3DIS) dataset is composed of the colored 3D point clouds of six large-scale indoor areas from three different buildings, each covering approximately 935, 965, 450, 1700, 870, and 1100 square meters (total of 6020 square meters). These areas show diverse properties in architectural style and appearance and include mainly office areas, educational and exhibition spaces, and conference rooms, personal offices, restrooms, open spaces, lobbies, stairways, and hallways are commonly found therein. The entire point clouds are automatically generated without any manual intervention using the Matterport scanner. The dataset also includes semantic instance annotations on the point clouds for 12 semantic elements, which are structural elements (ceiling, floor, wall, beam, column, window, and door) and commonly found items and furniture (table, chair, sofa, bookcase, and board).

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• This paper was presented in the "3D Semantic Parsing of Large-Scale Indoor Spaces", CVPR 2016.
• Project website: http://buildingparser.stanford.edu/

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Pointcept/s3dis-compressed dataset hosted on Hugging Face and contributed by the HF Datasets community

Abstract

2D-3D-S comprises

Methodology

The 2D-3D-S dataset provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. It covers over 6,000 m2 and contains over 70,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360° equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. In addition, the dataset contains the raw RGB and Depth imagery along with the corresponding camera information per scan location. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces.

In more detail, the dataset is collected in 6 large-scale indoor areas that originate from 3 different buildings of mainly educational and office use. For each area, all modalities are registered in the same reference system, yielding pixel to pixel correspondences among them. In a nutshell, the presented dataset contains a total of 70,496 regular RGB and 1,413 equirectangular RGB images, along with their corresponding depths, surface normals, semantic annotations, global XYZ OpenEXR format and camera metadata. It also contains the raw sensor data, which comprises of 18 HDR RGB and Depth images (6 looking forward, 6 towards the top, 6 towards the bottom) along with the corresponding camera metadata per each of the 1,413 scan locations, yielding a total of 25,434 RGBD raw images. In addition, we provide whole building 3D reconstructions as textured meshes, as well as the corresponding 3D semantic meshes. It also includes the colored 3D point cloud data of these areas with the total number of 695,878,620 points, that have been previously presented in the Stanford large-scale 3D Indoor Spaces Dataset (S3DIS).

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• The dataset is an extension of the Stanford large-scale 3D Indoor Spaces Dataset (S3DIS).
• Dataset files and structure: https://github.com/alexsax/2D-3D-Semantics
• Dataset information: http://3dsemantics.stanford.edu/

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