Self‐Calibrating Fisheye Lens Aberrations for Novel View Synthesis

Abstract Neural rendering techniques, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D‐GS), have led to significant advancements in scene reconstruction and novel view synthesis (NVS). These methods assume the use of an ideal pinhole model, which is free from lens distortion and optical aberrations. However, fisheye lenses introduce unavoidable aberrations due to their wide‐angle design and complex manufacturing, leading to multi‐view inconsistencies that compromise scene reconstruction quality. In this paper, we propose an end‐to‐end framework that integrates a standard 3D reconstruction pipeline with our lens aberration model to simultaneously calibrate lens aberrations and reconstruct 3D scenes. By modelling the real imaging process and jointly optimising both tasks, our framework eliminates the impact of aberration‐induced inconsistencies on reconstruction. Additionally, we propose a curriculum learning approach that ensures stable optimisation and high‐quality reconstruction results, even in the presence of multiple aberrations. To address the limitations of existing benchmarks, we introduce AbeRec, a dataset composed of scenes captured with lenses exhibiting severe aberrations. Extensive experiments on both existing public datasets and our proposed dataset demonstrate that our method not only significantly outperforms previous state‐of‐the‐art methods on fisheye lenses with severe aberrations but also generalises well to scenes captured by non‐fisheye lenses. Code and datasets are available at https://github.com/CPREgroup/Calibrating‐Fisheye‐Lens‐Aberration‐for‐NVS .