Research map / Point clouds / Segmentation and detection
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the full two-stage framework rather than training on initial annotations alone | MinkowskiUNet on SemanticKITTI · SemanticKITTI test | mIoU: 39.4 -> 44.8 | Helped |
| Apply the full two-stage framework rather than training on initial annotations alone | MinkowskiUNet on SemanticKITTI · SemanticKITTI test | mIoU: 46.4 -> 52.3 | Helped |
| Add temporal matching and spatial graph propagation to initial-annotation training | MinkowskiUNet on SemanticPOSS · SemanticPOSS area 3 | mIoU: 39.5 -> 52.2 | Helped |
| Add seed point propagation (ablation) | Backbone on SemanticPOSS · SemanticPOSS area 3 | mIoU: 39.5 -> 49.4 | Helped |
| Add dense scene propagation after seed point propagation (ablation) | Backbone on SemanticPOSS · SemanticPOSS area 3 | mIoU: 49.4 -> 52.2 | Helped |
| Add seed point propagation to the dense-stage configuration (ablation) | Model on SemanticKITTI validation · SemanticKITTI validation | mIoU: 45.4 -> 50.3; an mIoU boost of 4.9 | Helped |
| Use optimal-transport matching instead of greedy matching in the DSP stage (ablation) | Two-stage method on SemanticKITTI validation · SemanticKITTI validation | mIoU: 49.2 -> 50.3; a margin of 1.1 | Helped |
| Use optimal-transport matching instead of greedy matching in the SPP stage (ablation) | Seed point propagation on SemanticKITTI validation · SemanticKITTI validation | mIoU: 47.7 -> 47.9 | Helped |
| Use temporal matching with optimal transport rather than greedy matching (ablation) | Initial annotation training on SemanticKITTI validation · SemanticKITTI validation | mIoU: 40.9 -> 47.9 | Helped |
| Generate pseudo labels with high-confidence predictions rather than train on initial labels only | MinkowskiUNet on SemanticKITTI validation · SemanticKITTI validation | mIoU: 40.9 -> 42.6 | Helped |
Closest papers by meaning
- Weakly Supervised Point Cloud Semantic Segmentation via Artificial Oracle (CVPR 2024 · similarity 0.91)
- Less Is More: Label Recommendation for Weakly Supervised Point Cloud Semantic Segmentation (AAAI 2024 · similarity 0.90)
- Multi-Modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation (AAAI 2024 · similarity 0.89)
- Point Cloud Semantic Segmentation with Sparse and Inhomogeneous Annotations (AAAI 2025 · similarity 0.88)
- FreePoint: Unsupervised Point Cloud Instance Segmentation (CVPR 2024 · similarity 0.88)
- Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration (NeurIPS 2024 · similarity 0.88)
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