360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910719772983296 |
|---|---|
| author | Yan, Zhongmiao Wu, Qi Xia, Songpengcheng Deng, Junyuan Mu, Xiang Jin, Renbiao Pei, Ling |
| author_facet | Yan, Zhongmiao Wu, Qi Xia, Songpengcheng Deng, Junyuan Mu, Xiang Jin, Renbiao Pei, Ling |
| contents | 360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view affects feature extraction and matching, leading to geometric consistency issues in subsequent multi-view reconstruction. In this work, we propose 360Recon, an innovative MVS algorithm for ERP images. The proposed spherical feature extraction module effectively mitigates distortion effects, and by combining the constructed 3D cost volume with multi-scale enhanced features from ERP images, our approach achieves high-precision scene reconstruction while preserving local geometric consistency. Experimental results demonstrate that 360Recon achieves state-of-the-art performance and high efficiency in depth estimation and 3D reconstruction on existing public panoramic reconstruction datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19102 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images Yan, Zhongmiao Wu, Qi Xia, Songpengcheng Deng, Junyuan Mu, Xiang Jin, Renbiao Pei, Ling Computer Vision and Pattern Recognition 360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view affects feature extraction and matching, leading to geometric consistency issues in subsequent multi-view reconstruction. In this work, we propose 360Recon, an innovative MVS algorithm for ERP images. The proposed spherical feature extraction module effectively mitigates distortion effects, and by combining the constructed 3D cost volume with multi-scale enhanced features from ERP images, our approach achieves high-precision scene reconstruction while preserving local geometric consistency. Experimental results demonstrate that 360Recon achieves state-of-the-art performance and high efficiency in depth estimation and 3D reconstruction on existing public panoramic reconstruction datasets. |
| title | 360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.19102 |