SUM: A Benchmark Dataset of Semantic Urban Meshes
By Weixiao Gao, Liangliang Nan, B. Boom, Hugo Ledoux. 2021
79 citations recorded by the source corpus.
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. This paper introduces a new benchmark dataset of semantic urban meshes, a novel semi-automatic annotation framework, and an open-source 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 hours of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. Furthermore, we compare the performance of several representative 3D semantic segmentation methods on our annotated dataset. The results show our initial segmentation outperforms other methods and achieves an overall accuracy of 93.0% and mIoU of 66.2% with less training time compared to other deep learning methods. We also evaluate the effect of the input training data, which shows that our method only requires about 7% (which covers about 0.23 km2) to approach robust and adequate results whereas KPConv needs at least 33% (which covers about 1.0 km2).
Subfields: Computational Mechanics, Environmental Engineering, Ocean Engineering, 3D Vision & Pose
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Citations in the corpus
- 3D Instance Segmentation of MVS Buildings (2021, background): “Most of existing 3-D datasets are designed for semantic segmentation, such as Vaihingen3D [53], Swiss3DCities [54], Hessigheim3D [55], and Semantic Urban Meshes (SUM) [56].”