A hand-set modality guidance coefficient that steers a 2D/3D fusion network toward the more reliable input modality improves unsupervised domain adaptation for LiDAR semantic segmentation on four benchmarks.
Emerg- ing properties in self-supervised vision transformers
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Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation
A hand-set modality guidance coefficient that steers a 2D/3D fusion network toward the more reliable input modality improves unsupervised domain adaptation for LiDAR semantic segmentation on four benchmarks.