CLIPoint3D is the first CLIP-based framework for few-shot unsupervised 3D point cloud domain adaptation that reports 3-16% accuracy gains on PointDA-10 and GraspNetPC-10.
Multi-view convolutional neural networks for 3d shape recognition
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GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
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.
citing papers explorer
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CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation
CLIPoint3D is the first CLIP-based framework for few-shot unsupervised 3D point cloud domain adaptation that reports 3-16% accuracy gains on PointDA-10 and GraspNetPC-10.
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GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
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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.