SCSS-Net: Superpoint Constrained Semi-supervised Segmentation Network
for 3D Indoor Scenes
- URL: http://arxiv.org/abs/2107.03601v2
- Date: Fri, 9 Jul 2021 07:18:51 GMT
- Title: SCSS-Net: Superpoint Constrained Semi-supervised Segmentation Network
for 3D Indoor Scenes
- Authors: Shuang Deng, Qiulei Dong, and Bo Liu
- Abstract summary: We propose a superpoint constrained semi-supervised segmentation network for 3D point clouds, named as SCSS-Net.
Specifically, we use the pseudo labels predicted from unlabeled point clouds for self-training, and the superpoints produced by geometry-based and color-based Region Growing algorithms are combined to modify and delete pseudo labels with low confidence.
- Score: 6.3364439467281315
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Many existing deep neural networks (DNNs) for 3D point cloud semantic
segmentation require a large amount of fully labeled training data. However,
manually assigning point-level labels on the complex scenes is time-consuming.
While unlabeled point clouds can be easily obtained from sensors or
reconstruction, we propose a superpoint constrained semi-supervised
segmentation network for 3D point clouds, named as SCSS-Net. Specifically, we
use the pseudo labels predicted from unlabeled point clouds for self-training,
and the superpoints produced by geometry-based and color-based Region Growing
algorithms are combined to modify and delete pseudo labels with low confidence.
Additionally, we propose an edge prediction module to constrain the features
from edge points of geometry and color. A superpoint feature aggregation module
and superpoint feature consistency loss functions are introduced to smooth the
point features in each superpoint. Extensive experimental results on two 3D
public indoor datasets demonstrate that our method can achieve better
performance than some state-of-the-art point cloud segmentation networks and
some popular semi-supervised segmentation methods with few labeled scenes.
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