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- W3187324963 abstract "Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. The contributions of this work are three-fold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes. In particular, our dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we estimate that we save about 600 h of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. We also compare the performance of several state-of-the-art 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source." @default.
- W3187324963 created "2021-08-16" @default.
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- W3187324963 date "2021-09-01" @default.
- W3187324963 modified "2023-10-03" @default.
- W3187324963 title "SUM: A benchmark dataset of Semantic Urban Meshes" @default.
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- W3187324963 doi "https://doi.org/10.1016/j.isprsjprs.2021.07.008" @default.
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