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The research aims to review the advancements in 3D perception models utilizing the Mamba architecture and identify limitations.

Analysis of 3D Perception Models Based on the Mamba Architecture

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The research aims to review the advancements in 3D perception models utilizing the Mamba architecture and identify limitations.
Systematic review of recent research on 3D point cloud algorithms using Mamba architecture.
Analysis of challenges in applying State Space Models to 3D spatial data.
Evaluation of geometric information loss and interpretability in existing models.
The Mamba architecture shows improved processing efficiency for object detection and tracking in 3D point clouds.
Linear computational complexity offers advantages over traditional Transformer models.
Limitations in local feature preservation and interpretability are highlighted, suggesting areas for future research.

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Review of object detection and tracking methods in computer vision highlights linear computational complexity and interpretability challenges.

Zihao Li (2025) studied this question.

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Review of object detection and tracking methods in computer vision highlights linear computational complexity and interpretability challenges.