Rethinking Multimodal Few-Shot 3D Point Cloud Segmentation: From Fused Refinement to Decoupled Arbitration
arXiv:2601.01456v2 Announce Type: replace-cross Abstract: In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, CLIP's inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone a
This research addresses a critical limitation in current multimodal AI systems (like CLIP) when applied to complex 3D data, highlighting an active area of development in AI's foundational capabilities.
Improved 3D point cloud segmentation is crucial for advanced robotics, autonomous systems, and digital twins, enabling more robust and reliable real-world AI applications with less data.
By decoupling semantic and geometric paths, this work offers a potential pathway to more effective multimodal few-shot learning for 3D data, reducing the need for extensive annotated datasets in practical deployments.
- · AI research community
- · Robotics companies
- · Autonomous vehicle developers
- · 3D vision software providers
- · Companies reliant on large-scale 3D data annotation
Enhances the ability of AI models to understand and interact with the physical 3D world with limited training data.
Accelerates the development and deployment of intelligent robots and autonomous systems by reducing data collection and annotation bottlenecks.
Could lead to more sophisticated digital twin simulations and improved virtual-to-real transfer learning for industrial and defense applications.
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Read at arXiv cs.LG