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

• URL: http://arxiv.org/abs/2602.05321v1
• Date: Thu, 05 Feb 2026 05:42:03 GMT
• Title: Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning
• Authors: Dongki Jung, Jaehoon Choi, Adil Qureshi, Somi Jeong, Dinesh Manocha, Suyong Yeon,
• Abstract summary: We present Wid3R, a feed-forward neural network for visual geometry reconstruction that supports wide field-of-view camera models.<n>Our approach leverages a ray representation with spherical harmonics and a novel camera model token within the network, enabling distortion-aware 3D reconstruction.<n>It demonstrates strong zero-shot robustness and consistently outperforms prior methods, achieving improvements of up to +77.33 on Stanford2D3D.
• Score: 46.85601730573671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Wid3R, a feed-forward neural network for visual geometry reconstruction that supports wide field-of-view camera models. Prior methods typically assume that input images are rectified or captured with pinhole cameras, since both their architectures and training datasets are tailored to perspective images only. These assumptions limit their applicability in real-world scenarios that use fisheye or panoramic cameras and often require careful calibration and undistortion. In contrast, Wid3R is a generalizable multi-view 3D estimation method that can model wide field-of-view camera types. Our approach leverages a ray representation with spherical harmonics and a novel camera model token within the network, enabling distortion-aware 3D reconstruction. Furthermore, Wid3R is the first multi-view foundation model to support feed-forward 3D reconstruction directly from 360 imagery. It demonstrates strong zero-shot robustness and consistently outperforms prior methods, achieving improvements of up to +77.33 on Stanford2D3D.

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