SPHERA - A Unifying Structure from Motion Framework for Central Projection Cameras

Gava, Christiano Couto; Stricker, Didier

doi:10.5220/0005301702850293

Cited by 2 publications

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“…To enable a linear expression of the epipolar geometry, they use lifted coordinates for omnidirectional image points. Recently, Gava and Stricker [10] proposed a unified SFM framework that allows uniform treatment of central projection cameras using a sphere as the underlying model. This allows perspective, spherical and hybrid image datasets to be analysed.…”

Section: Related Workmentioning

confidence: 99%

Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution

Guan

Smith

2017

IEEE Trans. on Image Process.

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In this paper, we present a complete pipeline for computing structure-from-motion from the sequences of spherical images. We revisit problems from multiview geometry in the context of spherical images. In particular, we propose methods suited to spherical camera geometry for the spherical-n-point problem (estimating camera pose for a spherical image) and calibrated spherical reconstruction (estimating the position of a 3-D point from multiple spherical images). We introduce a new probabilistic interpretation of spherical structure-from-motion which uses the von Mises-Fisher distribution to model noise in spherical feature point positions. This model provides an alternate objective function that we use in bundle adjustment. We evaluate our methods quantitatively and qualitatively on both synthetic and real world data and show that our methods developed for spherical images outperform straightforward adaptations of methods developed for perspective images. As an application of our method, we use the structure-from-motion output to stabilise the viewing direction in fully spherical video.

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Section: Related Workmentioning

confidence: 99%

Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution

Guan

Smith

2017

IEEE Trans. on Image Process.

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3D point cloud reconstruction using panoramic images

Sharma,

Jain,

Shukla

2024

Appl Geomat

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Panorama photogrammetry, the process of analyzing panoramic images, has gained popularity in close-range photogrammetry for 3D reconstruction over the past decade. Initially, researchers utilized cylindrical or spherical panoramic images created through specialized cameras or conventional ones with rectilinear lenses. However, these methods were hindered by the high cost of panorama equipment and the need for manual reconstruction. Consequently, there's a growing demand for automated algorithms capable of reconstructing 3D point clouds from stitched panorama images. This study aims to provide a cost-effective solution for automatic 3D point cloud reconstruction from panoramas. The study is divided into two parts; it first outlines an image acquisition strategy for capturing overlapping perspective images to facilitate accurate panorama generation. Subsequently, it introduces an automated algorithm for 3D point cloud reconstruction from panorama images. The process utilizes the KAZE feature detector for feature detection and introduces a novel feature matching approach for matching panorama images. Accuracy assessment of the reconstructed 3D point clouds was done using three methods: Line Segment Based approach, yielding RMSE errors of 34.2mm and 39mm for dataset-1 and dataset-2 respectively, No-Reference 3D Point Cloud Quality Assessment, resulting in quality scores of 8.5939 and 7.4535 for dataset-1 and dataset-2 respectively, and M3C2 distance method computed value of 0.091059 and 0.165179 respectively, indicating high quality of the generated point clouds.

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SPHERA - A Unifying Structure from Motion Framework for Central Projection Cameras

Cited by 2 publications

References 0 publications

Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution

Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution

3D point cloud reconstruction using panoramic images

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