Research map / Label-efficient and robust learning / Domain adaptation and generalization
Single Domain Generalization for LiDAR Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add sparsity augmentation to the source-only training (ablation) | MinkUNet34 · SemanticKITTI source; unseen domains N, W, P | mIoU: 37.42 -> 40.27; 35.24 -> 38.16; 40.92 -> 45.68 | Helped |
| Add SIFC to sparsity augmentation (ablation) | MinkUNet34 · SemanticKITTI source; N | mIoU: 40.27 -> 44.32 | Helped |
| Add SIFC to sparsity augmentation (ablation) | MinkUNet34 · SemanticKITTI source; W | mIoU: 38.16 -> 38.14 | No clear effect |
| Add SIFC to sparsity augmentation (ablation) | MinkUNet34 · SemanticKITTI source; P | mIoU: 45.68 -> 42.31 | Hurt |
| Add SCC to the model with augmentation and SIFC (ablation) | MinkUNet34 · SemanticKITTI source; unseen domains N, W, P | mIoU: 44.32 -> 44.83; 38.14 -> 40.67; 42.31 -> 45.09 | Helped |
| Add SCC without SIFC to sparsity augmentation (ablation) | MinkUNet34 · SemanticKITTI source; W | mIoU: 38.16 -> 35.93 | Hurt |
| Use SIFC and SCC with sparsity augmentation | MinkUNet34 · SemanticKITTI source; K, N, W, P | mIoU: 57.31 -> 59.62; 37.42 -> 44.83; 35.24 -> 40.67; 40.92 -> 45.09 | Helped |
| Use SIFC and SCC with sparsity augmentation | MinkUNet34 · Waymo source; W, K, N, P | AM: 55.93 -> 57.60 | Helped |
Closest papers by meaning
- UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather (CVPR 2024 · similarity 0.89)
- Multi-Space Alignments Towards Universal LiDAR Segmentation (CVPR 2024 · similarity 0.88)
- LiT: Unifying LiDAR "Languages" with LiDAR Translator (NeurIPS 2024 · similarity 0.88)
- No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather (CVPR 2025 · similarity 0.88)
- D^3CTTA: Domain-Dependent Decorrelation for Continual Test-Time Adaption of 3D LiDAR Segmentation (CVPR 2025 · similarity 0.88)
- PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation (AAAI 2025 · similarity 0.88)
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