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Multi-View Stereo Vision Patchmatch Algorithm Based on Data Augmentation.

Feiyang Pan¹, Pengtao Wang¹, Lin Wang¹

¹School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces an efficient multi-view stereo vision patchmatch algorithm using data augmentation. It achieves faster processing and lower memory usage, making it suitable for high-resolution images and resource-constrained platforms.

Keywords:

adaptive propagation data augmentation multi-view stereo patchmatch

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Area of Science:

Computer Vision
Photogrammetry
3D Reconstruction

Background:

Multi-view stereo (MVS) algorithms are crucial for 3D reconstruction.
Traditional MVS methods often require significant computational resources and memory.
Existing patchmatch algorithms face challenges with high-resolution imagery and limited hardware.

Purpose of the Study:

To propose a novel multi-view stereo vision patchmatch algorithm.
To enhance processing speed and reduce memory consumption in 3D reconstruction.
To enable MVS on resource-constrained platforms.

Main Methods:

Implementation of a data augmentation module within an end-to-end multi-scale patchmatch framework.
Adoption of adaptive evaluation propagation to minimize memory overhead.
Efficient cascading of algorithm modules to optimize runtime and memory usage.

Main Results:

The proposed algorithm demonstrates competitive performance in terms of completeness and speed.
Significant reductions in runtime and computational memory compared to existing methods.
Successful processing of higher-resolution images and applicability on resource-constrained devices.

Conclusions:

The data augmentation-based MVS patchmatch algorithm offers an efficient solution for 3D reconstruction.
The method overcomes limitations of traditional region matching and 3D cost volume regularization approaches.
The algorithm is highly competitive for applications requiring speed, memory efficiency, and high-resolution processing.