PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
arXiv preprint arXiv:2403.00762 (2024)
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UNVERDICTED 5representative citing papers
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
3DMambaComplete applies the Mamba model to point cloud completion via hyperpoint generation, spatial spreading, and mesh deformation, claiming better results than prior methods on benchmarks.
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis
PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
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Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
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3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion
3DMambaComplete applies the Mamba model to point cloud completion via hyperpoint generation, spatial spreading, and mesh deformation, claiming better results than prior methods on benchmarks.
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MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.