Research map / Point clouds / Point cloud classification
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add local alignment and SA nodes to global MCD alignment (ablation) | PointDAN · PointDA-10 Avg | top-1 accuracy: 45.3 -> 48.7 | Helped |
| Add SA node module to global and local alignment (ablation) | PointDAN · PointDA-10 Avg | top-1 accuracy: 45.5 -> 47.5 | Helped |
| Add pseudo-label self-training (ablation) | PointDAN with G+L+A · PointDA-10 Avg | top-1 accuracy: 47.5 -> 48.7 | Helped |
| Add pseudo-label self-training (ablation) | PointDAN with G+L+A · ModelNet → ShapeNet | top-1 accuracy: 51.3 -> 51.0 | No clear effect |
| Use local alignment with fixed nodes instead of self-adaptive nodes (ablation) | PointDAN · PointDA-10 | top-1 accuracy | No clear effect |
| Use conv3 features for local alignment rather than conv1 or conv2 features (ablation) | PointDAN · ModelNet → ShapeNet; ScanNet → ModelNet | top-1 accuracy | Helped |
Closest papers by meaning
- Domain Adaptation on Point Clouds via Geometry-Aware Implicits (CVPR 2022 · similarity 0.92)
- SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection (CVPR 2021 · similarity 0.90)
- Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels (CVPR 2022 · similarity 0.90)
- DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion (AAAI 2025 · similarity 0.88)
- LinNet: Linear Network for Efficient Point Cloud Representation Learning (NeurIPS 2024 · similarity 0.88)
- Point2Real: Bridging the Gap between Point Cloud and Realistic Image for Open-World 3D Recognition (AAAI 2024 · similarity 0.88)
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