# 360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View

## TL;DR

This paper introduces a learning-based method to synthesize high-resolution 360-degree panoramas from a sparse set of conventional images with unknown field of view, enabling immersive scene exploration.

## Contribution

It presents a novel approach that estimates input image FOVs and synthesizes detailed 360 panoramas, outperforming existing methods and generalizing to non-panoramic scenes.

## Key findings

- Produces high-resolution 360 panoramas from few images
- Outperforms alternative methods in quality
- Generalizes to non-panoramic scenes and smartphone images

## Abstract

360 images represent scenes captured in all possible viewing directions and enable viewers to navigate freely around the scene thereby providing an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only certain parts of the scenes are observed, and valuable information about the surroundings is lost. In this paper, a learning-based approach that reconstructs the scene in 360 x 180 from a sparse set of conventional images (typically 4 images) is proposed. The proposed approach first estimates the FOV of input images relative to the panorama. The estimated FOV is then used as the prior for synthesizing a high-resolution 360 panoramic output. The proposed method overcomes the difficulty of learning-based approach in synthesizing high resolution images (up to 512$\times$1024). Experimental results demonstrate that the proposed method produces 360 panorama with reasonable quality. Results also show that the proposed method outperforms the alternative method and can be generalized for non-panoramic scenes and images captured by a smartphone camera.