基于全景图的深度融合重建方法，提升低纹理环境下的三维精度。
360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images
- 设计球面特征提取模块，缓解全景图像畸变影响
- 融合多尺度特征与3D代价体，实现高精度深度估计
- 适合虚拟现实、增强现实等全景重建场景
360度图像相比传统针孔相机具有更宽的视场，可在低纹理环境下实现稀疏采样和密集三维重建，对虚拟现实、增强现实等领域至关重要。然而，宽视场带来的固有畸变会影响特征提取与匹配，导致多视角重建中的几何不一致。本文提出360Recon，一种针对等距柱状投影（ERP）图像的新型多视图立体视觉（MVS）算法。所提出的球面特征提取模块有效缓解了畸变影响，并通过结合构建的3D代价体与来自ERP图像的多尺度增强特征，实现了高精度场景重建并保持局部几何一致性。实验结果表明，360Recon在现有公开全景重建数据集上达到了最先进的深度估计与三维重建性能，且具备高效率。
原文摘要 · Abstract (English)
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.
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