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The HDF5 profile for labeled point cloud data
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| Definitionskos:definition | Point cloud data are unstructured three-dimensional sample points to express the basic shape of objects and spaces. However, it is challenging to automatically generate continuous surfaces and infer semantic structures, such as cars, trees, buildings and roads, from a dataset of point clouds generated by a sensor. The understanding of the semantic structures is essential for recording geospatial information. Despite the good performance of deep learning-based approaches in understanding point clouds, their target coverage is still limited by the lack of training datasets that include semantic labels. This discussion paper addresses data formats to share a Labeled Point Cloud (LPC), in which point-level semantic information is annotated to each point. Creating LPCs manually or semi-manually is a time-consuming task. Therefore, sharing LPCs in an open standard format is becoming increasingly important for the development of more advanced deep learning algorithms for object detection, semantic segmentation, and instance segmentation. Even though several data formats are used to distribute LPC, there is a variety to represent the semantic information depending on distributors or domains. This discussion paper analyzes three popular formats of ASCII text, PLY, and LAS, for supporting LPC and finally proposes a practice to effectively apply HDF5 to facilitate the sharing and importing of LPC datasets. |
|---|---|
| Broaderbroader | Discussion Paper |
| http://purl.org/dc/terms/createdcreated | 2022-05-06 |
| Creatorcreator | Taehoon Kim, Wijae Cho, Kyoung-Sook Kim |
| seeAlsoseeAlso | https://docs.ogc.org/dp/21-077.html |
| Statusstatus | valid |
| Notationnotation | 21-077 |
| Alternative LabelaltLabel | The HDF5 profile for labeled point cloud data |
| 21-077 | |
| OGC document typedoctype | Discussion Paper |