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Published on: July 5, 2024

HiMamba-Net: a Hilbert-serialized Mamba network for 3D point cloud instance segmentation.

Kai Zhao¹, Dai Shi², Yi Guo³

¹Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, 2753, Australia. Kai.Zhao@westernsydney.edu.au.

Scientific Reports

|June 25, 2026

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This summary is machine-generated.

HiMamba-Net efficiently segments agricultural 3D point clouds using Hilbert curves and Mamba models. This approach achieves high accuracy in semantic and instance segmentation for large-scale field data.

Area of Science:

Computer Vision
Robotics
Agricultural Technology

Background:

Instance segmentation of 3D point clouds is challenging due to complex structures and occlusions.
Existing methods struggle with large-scale agricultural scenes featuring dense, irregular, and repetitive plant geometries.

Purpose of the Study:

To propose HiMamba-Net, an efficient framework for spatially coherent instance segmentation of agricultural point clouds.
To address limitations of current methods in handling the unique challenges of field-scale agricultural data.

Main Methods:

Utilizes Hilbert space-filling curves for spatial serialization, preserving geometric locality.
Employs selective state space models (SAMamba blocks) for linear-complexity sequential modeling.
Integrates patch-based feature extraction, multi-scale graph context aggregation, and multi-task learning.

Keywords:

Agricultural phenotyping Deep learning Hilbert curve Instance segmentation Point cloud segmentation State space models

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Published on: July 5, 2024

Main Results:

HiMamba-Net achieved 91.8% mIoU for semantic segmentation and 91.9% mAP for instance segmentation on the Crops3D dataset.
Outperformed baseline methods in accuracy and efficiency for agricultural point cloud segmentation.
Ablation studies confirmed the effectiveness of Hilbert serialization and selective state space modeling.

Conclusions:

Spatially coherent serialization combined with linear-complexity sequence modeling offers an effective solution for large-scale 3D point cloud instance segmentation.
HiMamba-Net demonstrates strong performance in complex agricultural environments.
The proposed framework advances the state-of-the-art in agricultural point cloud analysis.