PoolNet: Deep Learning for 2D to 3D Video Process Validation
This addresses the challenge of time-consuming and computationally expensive SfM data validation for researchers and practitioners in computer vision, though it appears incremental as it builds on existing validation methods.
The paper tackles the problem of inefficient and unreliable 2D to 3D video processing by introducing PoolNet, a deep learning framework that validates data for Structure-from-Motion (SfM) readiness, reducing processing time compared to state-of-the-art algorithms.
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.