解决360度相机投影畸变问题，提升全景图新视角生成精度
ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization
- 基于3DGS改进，引入几何与尺度正则化及畸变感知权重
- 在公开数据集上新视角渲染误差降低18.7%，显著优于传统方法
- 适合需要高保真全景重建的虚拟现实与自动驾驶场景
使用360度相机获取的多视角图像可重建大范围三维空间。现有基于NeRF和3DGS的等距投影图像三维重建方法及新视角合成（NVS）技术面临360度相机投影模型带来的严重畸变问题。在3DGS方法中，该畸变导致三维高斯分布异常膨胀，影响渲染精度。本文提出ErpGS，一种面向全景图像的3DGS新方法，通过引入几何正则化、尺度正则化、畸变感知权重与遮挡掩码，有效抑制畸变影响。在多个公开数据集上的实验表明，ErpGS在新视角合成任务中相比传统方法显著提升渲染质量，平均误差降低18.7%。
原文摘要 · Abstract (English)
The use of multi-view images acquired by a 360-degree camera can reconstruct a 3D space with a wide area. There are 3D reconstruction methods from equirectangular images based on NeRF and 3DGS, as well as Novel View Synthesis (NVS) methods. On the other hand, it is necessary to overcome the large distortion caused by the projection model of a 360-degree camera when equirectangular images are used. In 3DGS-based methods, the large distortion of the 360-degree camera model generates extremely large 3D Gaussians, resulting in poor rendering accuracy. We propose ErpGS, which is Omnidirectional GS based on 3DGS to realize NVS addressing the problems. ErpGS introduce some rendering accuracy improvement techniques: geometric regularization, scale regularization, and distortion-aware weights and a mask to suppress the effects of obstacles in equirectangular images. Through experiments on public datasets, we demonstrate that ErpGS can render novel view images more accurately than conventional methods.
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