An End to End Network Architecture for Fundamental Matrix Estimation

Abstract

In this paper, we present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images. To establish a complete working pipeline, different deep neural networks in charge of finding correspondences in images, performing outlier rejection and calculating fundamental matrix, are integrated into an end-to-end network architecture. To well train the network and preserve geometry properties of fundamental matrix, a new loss function is introduced. To evaluate the accuracy of estimated fundamental matrix more reasonably, we design a new evaluation metric which is highly consistent with visualization result. Experiments conducted on both outdoor and indoor data-sets show that this network outperforms traditional methods as well as previous deep learning based methods on various metrics and achieves significant performance improvements.

References in corpus (7)

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• DeMoN: Depth and Motion Network for Learning Monocular Stereo
• GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
• Deep Image Homography Estimation
• Learning Two-View Correspondences and Geometry Using Order-Aware Network
• An Evaluation of Feature Matchers for Fundamental Matrix Estimation
• Self-Improving Visual Odometry