PanDA is the first UDA method for multimodal 3D panoptic segmentation that improves robustness to single-modality degradation and pseudo-label completeness via asymmetric augmentation and dual-expert refinement.
Exploiting the complementarity of 2d and 3d networks to address domain-shift in 3d semantic segmentation
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UniD-Shift decomposes 2D and 3D features into shared semantic and private modality-specific subspaces to enable unified semantic segmentation with improved accuracy and cross-domain generalization on SemanticKITTI and nuScenes.
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PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving
PanDA is the first UDA method for multimodal 3D panoptic segmentation that improves robustness to single-modality degradation and pseudo-label completeness via asymmetric augmentation and dual-expert refinement.
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UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
UniD-Shift decomposes 2D and 3D features into shared semantic and private modality-specific subspaces to enable unified semantic segmentation with improved accuracy and cross-domain generalization on SemanticKITTI and nuScenes.