LiftProj: Space Lifting and Projection-Based Panorama Stitching
Yuan Jia, Ruimin Wu, Rui Song, Jiaojiao Li, Bin Song

TL;DR
LiftProj introduces a 3D space lifting and projection framework for panoramic stitching, significantly reducing distortions and ghosting in complex scenes with multiple depth layers and occlusions, outperforming traditional 2D methods.
Contribution
This paper presents a novel 3D space lifting and projection approach for panorama stitching, moving beyond 2D warping to achieve more geometrically consistent results in complex scenes.
Findings
Reduces geometric distortions in challenging scenes
Mitigates ghosting artifacts effectively
Produces more natural and consistent panoramic images
Abstract
Traditional image stitching techniques have predominantly utilized two-dimensional homography transformations and mesh warping to achieve alignment on a planar surface. While effective for scenes that are approximately coplanar or exhibit minimal parallax, these approaches often result in ghosting, structural bending, and stretching distortions in non-overlapping regions when applied to real three-dimensional scenes characterized by multiple depth layers and occlusions. Such challenges are exacerbated in multi-view accumulations and 360{\deg} closed-loop stitching scenarios. In response, this study introduces a spatially lifted panoramic stitching framework that initially elevates each input image into a dense three-dimensional point representation within a unified coordinate system, facilitating global cross-view fusion augmented by confidence metrics. Subsequently, a unified…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Robotics and Sensor-Based Localization
