PoolNet: Deep Learning for 2D to 3D Video Process Validation

TL;DR

PoolNet is a deep learning framework that efficiently validates 2D to 3D video data for structure-from-motion suitability, reducing processing time and improving data quality assessment.

Contribution

It introduces a novel deep learning approach for scene validation that is faster and more versatile than existing methods.

Findings

Successfully differentiates SfM-ready scenes from unfit data

Reduces processing time compared to state-of-the-art algorithms

Enhances data quality assessment for 2D to 3D video processing

Abstract

Lifting Structure-from-Motion (SfM) information from sequential and non-sequential image data is a time-consuming and computationally expensive task. In addition to this, the majority of publicly available data is unfit for processing due to inadequate camera pose variation, obscuring scene elements, and noisy data. To solve this problem, we introduce PoolNet, a versatile deep learning framework for frame-level and scene-level validation of in-the-wild data. We demonstrate that our model successfully differentiates SfM ready scenes from those unfit for processing while significantly undercutting the amount of time state of the art algorithms take to obtain structure-from-motion data.