Wo ist die Publikation erschienen?IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022
While most state-of-the-art instance segmentation meth-
ods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output.
This paper introduces PolyWorld, a neural network that di-
rectly extracts building vertices from an image and connects
them correctly to create precise polygons. The model pre-
dicts the connection strength between each pair of vertices
using a graph neural network and estimates the assign-
ments by solving a differentiable optimal transport problem.
Moreover, the vertex positions are optimized by minimiz-
ing a combined segmentation and polygonal angle differ-
ence loss. PolyWorld significantly outperforms the state of
the art in building polygonization and achieves not only no-
table quantitative results, but also produces visually pleas-
ing building polygons. Code and trained weights are pub-
licly available at https://github.com/zorzi-s/PolyWorldPretrainedNetwork.
Computer Vision, Pattern Recognition