ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization

• URL: http://arxiv.org/abs/2505.19883v2
• Date: Fri, 30 May 2025 04:15:01 GMT
• Title: ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization
• Authors: Shintaro Ito, Natsuki Takama, Koichi Ito, Hwann-Tzong Chen, Takafumi Aoki,
• Abstract summary: Multi-view images acquired by a 360-degree camera can reconstruct a 3D space with a wide area.<n>There are 3D reconstruction methods from equirectangular images based on NeRF and 3DGS, as well as Novel View Synthesis (NVS) methods.<n>We propose ErpGS, which is Omnidirectional GS based on 3DGS to realize NVS addressing the problems.
• Score: 9.784526657786342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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