High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow

TL;DR

This paper introduces a high-quality panorama stitching algorithm using asymmetric bidirectional optical flow tailored for fisheye lens images, achieving fast, near-seamless 360-degree panoramas suitable for real-time applications.

Contribution

The paper presents a novel panorama stitching method based on asymmetric bidirectional optical flow that handles different perspectives and parallax levels efficiently, leveraging GPU acceleration.

Findings

Produces high-quality 360-degree panoramas with minimal distortion.

Achieves stitching in under 30 seconds for high-resolution images.

Handles both distant and close perspective images effectively.

Abstract

In this paper, we propose a panorama stitching algorithm based on asymmetric bidirectional optical flow. This algorithm expects multiple photos captured by fisheye lens cameras as input, and then, through the proposed algorithm, these photos can be merged into a high-quality 360-degree spherical panoramic image. For photos taken from a distant perspective, the parallax among them is relatively small, and the obtained panoramic image can be nearly seamless and undistorted. For photos taken from a close perspective or with a relatively large parallax, a seamless though partially distorted panoramic image can also be obtained. Besides, with the help of Graphics Processing Unit (GPU), this algorithm can complete the whole stitching process at a very fast speed: typically, it only takes less than 30s to obtain a panoramic image of 9000-by-4000 pixels, which means our panorama stitching algorithm is of high value in many real-time applications. Our code is available at https://github.com/MungoMeng/Panorama-OpticalFlow.