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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Dual-Attention-Based Block Matching for Dynamic Point Cloud Compression.

Longhua Sun¹, Yingrui Wang¹, Qing Zhu²

¹School of Information Science and Engineering, Qilu Normal University, No. 2 Wenbo Road, Jinan 250200, China.

Journal of Imaging

|October 28, 2025

Summary

This summary is machine-generated.

This study introduces a hierarchical motion estimation/motion compensation (ME/MC) framework for dynamic three-dimensional (3D) point clouds (DPCs). The novel dual-attention-based KNN block-matching network significantly enhances inter-frame compression efficiency for DPCs.

Keywords:

3D reconstruction dynamic 3D point clouds geometric compression motion compensation motion estimation

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Area of Science:

Computer Vision and Graphics
Data Compression
Signal Processing

Background:

Dynamic three-dimensional (3D) point clouds (DPCs) exhibit irregular spatial distributions, complicating temporal context extraction and inter-frame compression.
Existing motion estimation/motion compensation (ME/MC) frameworks for DPCs are preliminary, with insufficient exploration of point consistency and temporal correlation.

Purpose of the Study:

To develop an advanced hierarchical ME/MC framework for improved dynamic 3D point cloud compression.
To enhance the accuracy of motion estimation for fine-grained inter-frame prediction in DPCs.

Main Methods:

Proposed a hierarchical ME/MC framework that adaptively selects motion field granularity for precise inter-frame prediction.
Introduced a dual-attention-based KNN block-matching (DA-KBM) network utilizing a bidirectional attention mechanism for accurate point correlation measurement.
Employed correlated points to predict inter-frame motion vectors, boosting prediction accuracy.

Main Results:

The proposed DPC compression method achieved a 70% BD-Rate gain over the V-PCC v13 standard on the 8iFVBv2 dataset.
Demonstrated a 16% gain over state-of-the-art deep learning-based inter-mode methods.

Conclusions:

The hierarchical ME/MC framework with DA-KBM significantly improves dynamic 3D point cloud compression performance.
The method offers a more effective approach to modeling temporal dependencies in DPCs compared to existing techniques.