Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction

• URL: http://arxiv.org/abs/2502.09563v1
• Date: Thu, 13 Feb 2025 18:15:10 GMT
• Title: Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction
• Authors: Youming Deng, Wenqi Xian, Guandao Yang, Leonidas Guibas, Gordon Wetzstein, Steve Marschner, Paul Debevec,
• Abstract summary: We present a self-calibrating framework that jointly optimize camera parameters, lens distortion and 3D Gaussian representations. Our technique enables high-quality scene reconstruction from Large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images.
• Score: 30.529707438964596
• License:
• Abstract: In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. In particular, our technique enables high-quality scene reconstruction from Large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. Our approach introduces a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortion, and demonstrates state-of-the-art performance on both synthetic and real-world datasets.

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