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Accelerating Point Cloud Computation via Memory in Embedded Structured Light Cameras.

Yanan Zhang^1,2, Shikang Meng^1,2, Shijie Wang^1,2

¹Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang 050081, China.

Journal of Imaging

|February 26, 2026

Summary

This summary is machine-generated.

This study introduces a memory-driven framework to accelerate structured light point cloud computation for embedded systems. By precomputing and storing parameters, it significantly speeds up 3D sensing without sacrificing accuracy.

Keywords:

3D reconstruction parameter calibration point cloud computation structured light camera

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Area of Science:

Computer Vision
3D Sensing
Embedded Systems

Background:

Structured light cameras are vital for 3D sensing but face computational limitations in embedded systems.
Achieving high-speed point cloud computation is challenging due to resource constraints.

Purpose of the Study:

To develop a memory-driven computational framework for accelerating structured light point cloud computation.
To reduce computational load in real-time 3D sensing applications.

Main Methods:

Precomputing the point cloud computation process and storing it as memory parameters.
Instantiating the framework into low-memory and high-memory methods.
Optimizing data formats to reduce bandwidth requirements.

Main Results:

The proposed methods achieve substantial speedups in point cloud computation.
Comparable accuracy to conventional methods is maintained.
Reduced computational load and memory footprint are demonstrated.

Conclusions:

The memory-driven framework offers a generalizable approach to optimize structured light pipelines.
Enables enhanced real-time 3D sensing in resource-constrained embedded applications.