Fast and Efficient Network for Field Disparity Estimation

Author / Creator

ICPR 2020 MAIN CONFERENCE (2021)

Conferences

ICPR 2020 MAIN CONFERENCE PS T5.1: Image and Audio Processing (2021)

Available as

Online

Summary

As with many imaging tasks, disparity estimation for light fields seems to be well-matched to machine learning approaches. Neural network-based methods can achieve an overall bad pixel rate as low ...

As with many imaging tasks, disparity estimation for light fields seems to be well-matched to machine learning approaches. Neural network-based methods can achieve an overall bad pixel rate as low as four percent on the 4D light field benchmark dataset, continued effort to improve accuracy is resulting in diminishing returns. On the other hand, due to the growing importance of mobile and embedded devices, improving efficiency is emerging as an important problem. In this paper, we improve the efficiency of existing neural net approaches for light field disparity estimation by introducing efficient network blocks, pruning redundant sections of the network, and downsampling the resolution of the feature vector. To improve performance, we also propose densely sampled epipolar image plane volumes as input. Experiment results show that our approach can achieve similar results compared with state-of-the-art methods while using only one-tenth runtime.

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