DM3D: Dynamic Mamba via Offset-Guided Feature Resampling for Point Cloud Understanding

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

DM3D is a deformable Mamba architecture designed for point cloud understanding, addressing the challenge of irregular point cloud structures conflicting with State Space Models' (SSMs) reliance on input order. Developed by Bin Liu, Chunyang Wang, and Xuelian Liu from Xi'an Technological University, DM3D introduces an offset-guided Gaussian sequencing mechanism. This mechanism unifies local resampling via Gaussian-based KNN Resampling (GKR) and global reordering through Gaussian-based Differentiable Reordering (GDR), enabling structure-adaptive serialization. Additionally, a Tri-Path Frequency Fusion module enhances feature complementarity. Experiments on ModelNet40, ScanObjectNN, and ShapeNetPart show DM3D achieves highly competitive performance in classification (e.g., 93.76% on ModelNet40 without pre-training), few-shot learning, and part segmentation (84.6% Cls.mIoU, 86.2% Ins.mIoU with pre-training).

Key takeaway

For machine learning engineers developing 3D vision systems, DM3D demonstrates that dynamic, learnable serialization is crucial for effectively applying State Space Models to point clouds. You should consider integrating deformable mechanisms like offset-guided Gaussian sequencing into your Mamba-based architectures to adapt to irregular geometries. This approach improves performance in classification, few-shot learning, and segmentation by optimizing point ordering and spatial sampling end-to-end, rather than relying on static preprocessing.

Key insights

Dynamic, learnable serialization via deformable Mamba enhances point cloud understanding by adapting to geometric structures.

Principles

• Point cloud serialization should be learned.
• Gaussian kernels unify resampling and reordering.
• Frequency domain fusion reduces aliasing.

Method

DM3D uses an offset-guided Gaussian sequencing mechanism with Gaussian-based KNN Resampling (GKR) for local adaptivity and Gaussian-based Differentiable Reordering (GDR) for end-to-end sequence optimization, complemented by Tri-Path Frequency Fusion.

In practice

• Apply GKR with K_r=3 for optimal local aggregation.
• Use GDR with σ_t=0.2 for balanced sorting smoothness.
• Integrate frequency fusion for multi-path SSMs.

Topics

• Point Cloud Understanding
• State Space Models
• Mamba Architecture
• Deformable Networks
• Gaussian Kernels
• 3D Classification
• Part Segmentation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.