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Context-Aware 3D Point Cloud Semantic Segmentation With Plane Guidance
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DOI：10.1109/TMM.2022.3212914.png)
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Point cloud segmentation is fundamental in understanding 3D environments. However, most existing methods usually perform poorly on identifying boundaries of touching objects and large surfaces of objects. Planes in a scene usually act as supporting surfaces to separate touching objects and provide geometry priors to group points on a large surface as shown in Fig. 1. Besides, planes can roughly represent the structure of a scene, and are more efficient to encode holistic scene contexts than large scale point clouds. In light of the above advantages, we advise a plane-assisted module, coined 3D-PAM, to enhance semantic segmentation of touching objects and large surface objects. 3D-PAM consists of a plane separation network (PS-Net) and a plane relation network (PR-Net). PS-Net focuses on learning features that can robustly separate touching objects, e.g., a chair on a floor, as well as capture plane-based geometry priors to group points on a large plane, e.g., points of a desk. PR-Net encodes mutual plane relations as a proxy of a scene structure to capture holistic contexts. 3D-PAM is designed as a plug-and-play module so that it can be easily plugged into any off-the-shelf semantic segmentation network. Extensive experiments demonstrate that the method achieves large segmentation improvements on several backbones, and accomplishes superior results on most categories when using a RandLA-Net backbone (11/13 categories on S3DIS dataset and 15/20 categories on ScanNetv2 dataset). The project is available at GitHub https://github.com/windmillknight/ Context- Aware- 3DPoint- Cloud- Semantic-Segmentation-With- Plane- Guidance
Keyword:
Three-dimensional displays
Point cloud compression
Geometry
Shape
Semantics
Task analysis
Network architecture
3D semantic segmentation
holistic contexts
point cloud
plane geometry
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9.7
论文数:
4.5K
被引数:
2.4W
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