360Recon: An Accurate Reconstruction Method based on Depth Fusion from 360 Images
Zhongmiao Yan, Qi Wu, Songpengcheng Xia, Junyuan Deng, Xiang Mu, Renbiao Jin, Changchun Ye, Ling Pei
Abstract
Accurate 3D reconstruction is crucial for AR and VR applications. Compared with traditional pinhole camera-based methods, 360° image-based reconstruction can achieve higher precision with fewer input images, making it especially effective in low-texture environments. However, the severe distortion resulting from the wide field of view complicates feature extraction and matching, leading to geometric inconsistencies in multi-view reconstruction. To address these challenges, we propose 360Recon, a novel multi-view stereo (MVS) algorithm specifically designed for equirectangular projection (ERP) images. With the proposed spherical feature extraction module mitigating distortion, 360Recon integrates a 3D cost volume with multi-scale ERP features to deliver high-precision scene reconstruction while preserving local geometric consistency. Experimental results demonstrate that 360Recon outperforms existing methods in terms of accuracy, computational efficiency, and generalization capability. The source code will be released at https://github.com/LeonATP/360Recon.
BibTeX
@inproceedings{iros2025_360reconanaccura,
title = {360Recon: An Accurate Reconstruction Method based on Depth Fusion from 360 Images},
author = {Zhongmiao Yan and Qi Wu and Songpengcheng Xia and Junyuan Deng and Xiang Mu and Renbiao Jin and Changchun Ye and Ling Pei},
booktitle = {IROS 2025},
year = {2025}
}