AccidentalGS: 3D Gaussian Splatting from Accidental Camera Motion

Neural 3D modeling and novel view synthesis with Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) typically requires the multi-view images with wide baselines and accurate camera poses as input. However, scenarios with accidental camera motions are rarely studied. In this paper, we propose AccidentalGS , the first method for neural 3D modeling and novel view synthesis from accidental camera motions. To achieve this, we present a novel joint optimization framework that considers geometric and photometric errors, using a simplified camera model for stability. We also introduce a novel online adaptive depth-consistency loss to prevent the overfitting of the Gaussian model to input images. Extensive experiments on both synthetic and real-world datasets show that AccidentalGS achieves more accurate camera poses and realistic novel views compared to existing methods, and supports 3D modeling and neural rendering even for the Moon with telescope-like images.