SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution

TL;DR

SGNet enhances depth map super-resolution by integrating gradient and frequency domain information, using modules that sharpen structure and propagate high-frequency details from RGB images, achieving state-of-the-art results.

Contribution

Introduces a novel structure guided network that leverages gradient and frequency domain modules for improved depth super-resolution.

Findings

Outperforms existing methods on real and synthetic datasets.

Effectively captures high-frequency structures in depth maps.

Achieves state-of-the-art super-resolution performance.

Abstract

Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure. However, since the structure of LR depth is usually blurry, only considering spatial domain is not very sufficient to acquire satisfactory results. In this paper, we propose structure guided network (SGNet), a method that pays more attention to gradient and frequency domains, both of which have the inherent ability to capture high-frequency structure. Specifically, we first introduce the gradient calibration module (GCM), which employs the accurate gradient prior of RGB to sharpen the LR depth structure. Then we present the Frequency Awareness Module (FAM) that recursively conducts multiple spectrum differencing blocks (SDB), each of which propagates the precise high-frequency components of RGB into the LR depth. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our SGNet, reaching the state-of-the-art. Codes and pre-trained models are available at https://github.com/yanzq95/SGNet.