Attention-based Multi-Level Fusion Network for Light Field Depth Estimation

Authors

  • Jiaxin Chen School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China
  • Shuo Zhang School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China
  • Youfang Lin School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China Key Laboratory of Transport Industry of Big Data Appalication Technologies for Comprehensive Transport, Beijing, China

Keywords:

3D Computer Vision, Computational Photography, Image & Video Synthesis, Low Level & Physics-based Vision, (Deep) Neural Network Algorithms

Abstract

Depth estimation from Light Field (LF) images is a crucial basis for LF related applications. Since multiple views with abundant information are available, how to effectively fuse features of these views is a key point for accurate LF depth estimation. In this paper, we propose a novel attention-based multi-level fusion network. Combined with the four-branch structure, we design intra-branch fusion strategy and inter-branch fusion strategy to hierarchically fuse effective features from different views. By introducing the attention mechanism, features of views with less occlusions and richer textures are selected inside and between these branches to provide more effective information for depth estimation. The depth maps are finally estimated after further aggregation. Experimental results shows the proposed method achieves state-of-the-art performance in both quantitative and qualitative evaluation, which also ranks first in the commonly used HCI 4D Light Field Benchmark.

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Published

2021-05-18

How to Cite

Chen, J., Zhang, S., & Lin, Y. (2021). Attention-based Multi-Level Fusion Network for Light Field Depth Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1009-1017. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16185

Issue

Section

AAAI Technical Track on Computer Vision I