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vLAR-group/GrowSP ★ 200 18 APPLICATION

GrowSP is an unsupervised semantic segmentation framework for 3D point clouds that learns from growing superpoints. The system processes raw point cloud data through preparation pipelines, generates initial superpoints using voxelization and region growing, then trains a Feature Pyramid Network (Res16FPN18) to learn discriminative features. During training, superpoints are progressively merged (growing superpoints strategy) to create hierarchical representations, with pseudo-labels generated via clustering of superpoint features. The framework includes specialized dataset classes for S3DIS, ScanNet, and SemanticKITTI, evaluation scripts using Hungarian matching for unsupervised performance measurement, and utility libraries for 3D augmentations and PLY file handling. The architecture follows a modular design with clear separation between data preparation, dataset handling, model definitions, and training/evaluation logic.

RATING: 7.9/10 MAINT: active DRIFT: medium
#unsupervised-seg #point-clouds #superpoints #growsp #minkowskiengine #fpn #pytorch #3d #3d-scene-understanding #3d-semantic-segmentation #geometric-deep-learning #pointcloud #self-driving #unsupervised-deep-learning #unsupervised-learning
▸ 4 reusable components found
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