Point-Focused Attention Meets Context-Scan State Space: Robust Biological Visual Perception for Point Cloud Representation

arXiv:2610.11342 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: Accepted by ICLR'26

Code: https://github.com/Point-Cloud-Learning/PointLearner

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Point Clouds — 30× in this paper · explained in 2 episodes
• Transformer — 30× in this paper · explained in 2 episodes
• Point-Focused Attention — 24× in this paper
• Ablation — 19× in this paper · explained in 1 episode
• Robustness — 14× in this paper · explained in 5 episodes
• Representation Learning — 13× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Attention Mechanism — 11× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 7× in this paper
• Accuracy — 6× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 5× in this paper
• Computational overhead — 5× in this paper · explained in 2 episodes
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• Core — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Transformer Models — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Computational Visual Media — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• European Conference on Computer Vision — 2× in this paper
• Farthest Point Sampling — 2× in this paper
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Contextual Scan State Space — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Point Cloud Data — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Remote Sensing — 1× in this paper
• Sparsity — 1× in this paper · explained in 4 episodes
• Transactions on Graphics — 1× in this paper
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs
• VideoMamba: State Space Model for Efficient Video Understanding
• Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy
• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
• VM-UNet: Vision Mamba UNet for Medical Image Segmentation
• STREAM: A Universal State-Space Model for Sparse Geometric Data
• PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
• SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
• Point Cloud Mamba: Point Cloud Learning via State Space Model

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