Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning

We present Wid3R, a feed-forward neural network for multi-view visual geometry reconstruction that supports wide field-of-viewcamera models. Unlike existing methods that assume rectified or pinholeinputs, Wid3R directly models wide-angle imagery without explicit cali-bration or undistortion. Our approach leverages a ray-based representa-tion with spherical harmonics and introduces a novel camera model tokento enable distortion-aware reconstruction. To the best of our knowledge,Wid3R is the first multi-frame feed-forward 3D reconstruction methodthat supports 360◦ imagery. Moreover, we show that conditioning on di-verse camera types improves generalization to 360◦ scenes and alleviatesdata sparsity issues. Wid3R achieves significant performance gains, im-proving AUC@30◦ by up to +33.67 on Zip-NeRF (fisheye) and +77.33 onStanford2D3D (360). Project Page: https://jdk9405.github.io/Wid3R/