EPINET-Lite: Rethinking Mixed Convolutions for Efficient Light Field Disparity Estimation Network

Abstract

Convolutional neural networks are widely used for light field disparity estimation. However, many state-of-the-art deep learning models are computationally expensive due to their reliance on standard convolutions with varying kernel sizes. In this paper, we analyze the effect of various advanced convolution operations with different kernel sizes for feature extraction in a state-of-the-art light field disparity estimation network. Based on this investigation, we propose an optimized mixed convolution layer to extract relevant features using multiple kernel sizes in parallel, while maintaining significantly lower computational cost. Experimental results demonstrate that our approach reduces model complexity by up to 4.2× while also improving disparity estimation accuracy. These findings make the proposed convolutional operation more practical for light field applications, where efficient spatial and angular feature extraction is essential for improved model performance.