AAAI

Association for the Advancement of Artificial Intelligence

Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 2: AAAI-21 Technical Tracks 2

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


Proceedings:

No. 2: AAAI-21 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v35i2.16185


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35