Employment of conditional random fields for monocular depth estimation
Year: 2024
Type: conference-paper
Abstract: Estimating depth from a solitary RGB image, known as monocular depth estimation, presents a significant challenge. Currently, most methods for this task involve designing increasingly complex networks to regress the depth map straightforwardly. However, we have adopted a more interpretable approach by using Conditional Random Fields from optimization methods. Additionally, to facilitate better information transfer between nodes, a multi-head attention mechanism is employed to calculate multiple energy functions, which are then optimized by the network into an accurate depth map. Experiments demonstrate that our method can accurately estimate the depth of landscapes.
Authors: Xudong Zhang, Guosong Zhu
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401519055