Structure-Aware Pre-Selected Neural Rendering for Light Field Reconstruction

Song Chang, Youfang Lin, Shuo Zhang

Abstract: As densely-sampled Light Field (LF) images are beneficial to many applications, LF reconstruction becomes an important technology in related fields. Recently, neural rendering shows great potential in reconstruction tasks. However, volume rendering in existing methods needs to sample many points on the whole camera ray or epipolar line, which is time-consuming. In this paper, specifically for LF images with regular angular sampling, we propose a novel Structure-Aware Pre-Selected neural rendering framework for LF reconstruction. Instead of sampling on the whole epipolar line, we propose to sample on several specific positions, which are estimated using the color and inherent scene structure information explored in the regular angular sampled LF images. Sampling only a few points that closely match the target pixel, the feature of the target pixel is quickly rendered with high-quality. Finally, we fuse the features and decode them in the view dimension to obtain the final target view. Experiments show that the proposed method outperforms the state-of-the-art LF reconstruction methods in both qualitative and quantitative comparisons across various tasks. Our method also surpasses the most existing methods in terms of speed. Moreover, without any retraining or fine-tuning, the performance of our method with no-per-scene optimization is even better than the methods with per-scene optimization.

External IDs:dblp:journals/tmm/ChangLZ25