FullCircle: Effortless 3D Reconstruction from Casual 360$^\circ$ Captures
Yalda Foroutan, Ipek Oztas, Daniel Rebain, Aysegul Dundar, Kwang Moo Yi, Lily Goli, Andrea Tagliasacchi

TL;DR
FullCircle presents a practical, robust pipeline for 3D scene reconstruction directly from casual 360-degree camera captures, eliminating the need for special protocols or pre-processing, and outperforming existing methods.
Contribution
It introduces a novel, easy-to-use reconstruction pipeline for raw 360-degree images that handles common errors and sets a new benchmark with a dedicated dataset.
Findings
Outperforms vanilla 3DGS for 360° cameras
Robust to human presence in images
Outperforms perspective baselines when simulating perspective from 360° data
Abstract
Radiance fields have emerged as powerful tools for 3D scene reconstruction. However, casual capture remains challenging due to the narrow field of view of perspective cameras, which limits viewpoint coverage and feature correspondences necessary for reliable camera calibration and reconstruction. While commercially available 360 cameras offer significantly broader coverage than perspective cameras for the same capture effort, existing 360 reconstruction methods require special capture protocols and pre-processing steps that undermine the promise of radiance fields: effortless workflows to capture and reconstruct 3D scenes. We propose a practical pipeline for reconstructing 3D scenes directly from raw 360 camera captures. We require no special capture protocols or pre-processing, and exhibit robustness to a prevalent source of reconstruction errors: the human…
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Taxonomy
TopicsAdvanced Vision and Imaging · Computer Graphics and Visualization Techniques · Optical measurement and interference techniques
