GeGS-PCR: Effective and Robust 3D Point Cloud Registration with Two-Stage Color-Enhanced Geometric-3DGS Fusion

• URL: http://arxiv.org/abs/2604.17721v1
• Date: Mon, 20 Apr 2026 02:14:41 GMT
• Title: GeGS-PCR: Effective and Robust 3D Point Cloud Registration with Two-Stage Color-Enhanced Geometric-3DGS Fusion
• Abstract summary: We propose GeGS-PCR, a novel two-stage method that combines geometric, color, and Gaussian information for robust registration.<n>Our approach incorporates a dedicated color encoder that enhances color features by extracting multi-level geometric and color data.<n>Our method achieves state-of-the-art performance with textitRegistration Recall at 99.9%, textitRelative Rotation Error as low as 0.013, and textitRelative Translation Error as low as 0.024, improving precision by at least a factor of 2.
• Score: 9.175910719048845
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We address the challenge of point cloud registration using color information, where traditional methods relying solely on geometric features often struggle in low-overlap and incomplete scenarios. To overcome these limitations, we propose GeGS-PCR, a novel two-stage method that combines geometric, color, and Gaussian information for robust registration. Our approach incorporates a dedicated color encoder that enhances color features by extracting multi-level geometric and color data from the original point cloud. We introduce the \textbf{Ge}ometric-3D\textbf{GS} module, which encodes the local neighborhood information of colored superpoints to ensure a globally invariant geometric-color context. Leveraging LORA optimization, we maintain high performance while preserving the expressiveness of 3DGS. Additionally, fast differentiable rendering is utilized to refine the registration process, leading to improved convergence. To further enhance performance, we propose a joint photometric loss that exploits both geometric and color features. This enables strong performance in challenging conditions with extremely low point cloud overlap. We validate our method by colorizing the Kitti dataset as ColorKitti and testing on both Color3DMatch and Color3DLoMatch datasets. Our method achieves state-of-the-art performance with \textit{Registration Recall} at 99.9\%, \textit{Relative Rotation Error} as low as 0.013, and \textit{Relative Translation Error} as low as 0.024, improving precision by at least a factor of 2.

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