Efficient two-view geometry classification

Abstract

Typical Structure-from-Motion systems spend major computational effort on geometric verification. Geometric verification recovers the epipolar geometry of two views for a moving camera by estimating a fundamental or essential matrix. The essential matrix describes the relative geometry for two views up to an unknown scale. Two-view triangulation or multi-model estimation approaches can reveal the relative geometric configuration of two views, e.g., small or large baseline and forward or sideward motion. Information about the relative configuration is essential for many problems in Structure-from-Motion. However, essential matrix estimation and assessment of the relative geometric configuration are computationally expensive. In this paper, we propose a learning-based approach for efficient two-view geometry classification, leveraging the by-products of feature matching. Our approach can predict whether two views have scene overlap and for overlapping views it can assess the relative geometric configuration. Experiments on several datasets demonstrate the performance of the proposed approach and its utility for Structure-from-Motion.