An End to End Network Architecture for Fundamental Matrix Estimation

• URL: http://arxiv.org/abs/2010.15528v1
• Date: Thu, 29 Oct 2020 12:48:43 GMT
• Title: An End to End Network Architecture for Fundamental Matrix Estimation
• Authors: Yesheng Zhang, Xu Zhao, Dahong Qian
• Abstract summary: We present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images. 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.
• Score: 14.297068346634351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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