When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, 00, pp. 3707-3716
- Issue Date:
- 2024-04-09
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1723511.pdf | Published version | 2.21 MB |
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Learning from bounding boxes annotations has shown great potential in weakly supervised 3D point cloud instance segmentation However we observed that existing methods would suffer severe performance degradation with perturbed bounding box annotations To tackle this issue we propose a complementary image prompt induced weakly supervised point cloud instance segmentation CIP WPIS method CIP WPIS leverages pretrained knowledge embedded in the 2D foundation model SAM and 3D geometric prior to achieve accurate point wise instance labels from the bounding box annotations Specifically CIP WPIS first selects image views in which 3D candidate points of an instance are fully visible Then we generate complementary background and foreground prompts from projections to obtain SAM 2D instance mask predictions According to these we assign the confidence values to points indicating the likelihood of points belonging to the instance Furthermore we utilize 3D geometric homogeneity provided by superpoints to decide the final instance label assignments In this fashion we achieve high quality 3D point wise instance labels Extensive experiments on both Scannet v2 and S3DIS benchmarks proves that our method not only achieves state of the art performance for bounding boxes supervised point cloud instance segmentation but also exhibits robustness against noisy 3D bounding box annotations
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