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<p>3D reconstruction from multi-view RGB images is important for computer vision and computer graphics applications. Recently, neural surface reconstruction methods have shown promising results for reconstructing detailed 3D geometry in complex scenes. These methods can complement classical multi-view stereo approaches, especially under challenging appearance conditions such as non-Lambertian surfaces and thin structures. A common assumption for these methods is the availability of accurate multi-view camera parameters, which limits their applicability to real-world problems. In this paper, we present bundle-adjusting NeuS (BA-NeuS), a neural surface reconstruction method that integrates the core principle of BA, the joint and simultaneous optimization of 3D structure and camera parameters, directly into the neural reconstruction pipeline. Building on NoPose-NeuS and NeuS, our method performs this BA by jointly optimizing: (1) the implicit neural surface representation (the structure) and (2) the multi-view camera parameters (the motion). To achieve this, we represent the camera parameters, including not only poses but also intrinsics (focal length and optical center), as a multi-layer perceptron. We propose an additional multi-view point cloud alignment loss function that constrains this joint optimization and stabilizes camera-parameter estimation. Our experiments on object-level and scene-level datasets show that the proposed method can reconstruct plausible scene surfaces while estimating camera parameters in the evaluated settings. Compared with baselines that also estimate camera parameters, BA-NeuS gives a modest average improvement on DTU, achieving a mean Chamfer distance of 0.86 compared with 0.89 for NoPose-NeuS, while additionally optimizing camera intrinsics. The code is available at: <a href="https://github.com/DarkGeekMS/bundle-adjusting-neus">https://github.com/DarkGeekMS/bundle-adjusting-neus</a>.</p>

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Ba-neus: joint optimization of neural implicit camera and geometry representations for multiview 3D reconstruction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Mohamed Shawky Sabae,
  • Hoda Anis Baraka,
  • Mayada Mansour Hadhoud

摘要

3D reconstruction from multi-view RGB images is important for computer vision and computer graphics applications. Recently, neural surface reconstruction methods have shown promising results for reconstructing detailed 3D geometry in complex scenes. These methods can complement classical multi-view stereo approaches, especially under challenging appearance conditions such as non-Lambertian surfaces and thin structures. A common assumption for these methods is the availability of accurate multi-view camera parameters, which limits their applicability to real-world problems. In this paper, we present bundle-adjusting NeuS (BA-NeuS), a neural surface reconstruction method that integrates the core principle of BA, the joint and simultaneous optimization of 3D structure and camera parameters, directly into the neural reconstruction pipeline. Building on NoPose-NeuS and NeuS, our method performs this BA by jointly optimizing: (1) the implicit neural surface representation (the structure) and (2) the multi-view camera parameters (the motion). To achieve this, we represent the camera parameters, including not only poses but also intrinsics (focal length and optical center), as a multi-layer perceptron. We propose an additional multi-view point cloud alignment loss function that constrains this joint optimization and stabilizes camera-parameter estimation. Our experiments on object-level and scene-level datasets show that the proposed method can reconstruct plausible scene surfaces while estimating camera parameters in the evaluated settings. Compared with baselines that also estimate camera parameters, BA-NeuS gives a modest average improvement on DTU, achieving a mean Chamfer distance of 0.86 compared with 0.89 for NoPose-NeuS, while additionally optimizing camera intrinsics. The code is available at: https://github.com/DarkGeekMS/bundle-adjusting-neus.