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Related Experiment Video

Updated: Aug 7, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Fully Cross-Attention Transformer for Guided Depth Super-Resolution.

Ido Ariav¹, Israel Cohen¹

¹Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel transformer network for depth map super-resolution, improving accuracy by effectively using color images for guidance. The new method overcomes texture copying issues common in existing depth super-resolution techniques.

Keywords:

attention deep learning depth maps multimodal super-resolution transformers

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Low spatial resolution of modern depth sensors limits practical applications.
High-resolution color images often accompany low-resolution depth maps.
Existing learning-based depth super-resolution methods suffer from texture copying issues.

Purpose of the Study:

To develop an advanced learning-based method for depth map super-resolution.
To address the texture copying problem in guided depth super-resolution.
To propose a fully transformer-based network for enhanced depth map quality.

Main Methods:

A novel fully transformer-based network architecture is proposed.
A cascaded transformer module extracts features from low-resolution depth data.
A cross-attention mechanism integrates color image guidance into depth upsampling.
A window partitioning scheme ensures linear complexity for high-resolution image application.

Main Results:

The proposed method effectively guides depth upsampling using color images.
Texture copying artifacts are significantly reduced compared to existing methods.
Extensive experiments demonstrate superior performance over state-of-the-art techniques.

Conclusions:

The fully transformer-based network offers a robust solution for guided depth super-resolution.
The novel cross-attention mechanism enhances the integration of color and depth information.
The method achieves state-of-the-art results, enabling more accurate depth map generation.