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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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This repository contains the **VX-S3DIS** dataset, presented in [RESSCAL3D++](https://arxiv.org/abs/2410.02323), an indoor scene dataset which accurately mimics the behavior of a resolution-scalable 3D sensor. The presented dataset is the first dataset leveraging resolution scalable point streams. It is build upon the S3DIS dateset and comprises a total of 7031 samples derived from 168 distinct rooms within the S3DIS dataset. The dataset includes annotations for 11 different classes: floor, wall, column, window, door, table, chair, sofa, bookcase, board, and clutter. |
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The data is distributed as ply files where all information is encoded in the vertex attributes. Please see DATA.md for details about the data. |
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If you use this data, please cite the [RESSCAL3D++](https://arxiv.org/abs/2410.02323) paper along with the S3DIS paper. |
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``` |
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@inproceedings{royen2024resscal3d++, |
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title={RESSCAL3D++: Joint Acquisition and Semantic Segmentation of 3D Point Clouds}, |
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author={Royen, Remco and Pataridis, Kostas and van der Tempel, Ward and Munteanu, Adrian}, |
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booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, |
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pages={3547--3553}, |
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year={2024}, |
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organization={IEEE} |
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} |
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``` |
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``` |
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@inproceedings{armeni20163d, |
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title={3d semantic parsing of large-scale indoor spaces}, |
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author={Armeni, Iro and Sener, Ozan and Zamir, Amir R and Jiang, Helen and Brilakis, Ioannis and Fischer, Martin and Savarese, Silvio}, |
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
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pages={1534--1543}, |
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year={2016} |
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} |
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``` |
