Self-Calibrating Neural Radiance Fields (2108.13826v2)

Abstract: In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions. While traditional self-calibration algorithms mostly rely on geometric constraints, we additionally incorporate photometric consistency. This requires learning the geometry of the scene, and we use Neural Radiance Fields (NeRF). We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models. We validate our approach on standard real image datasets and demonstrate that our model can learn the camera intrinsics and extrinsics (pose) from scratch without COLMAP initialization. Also, we show that learning accurate camera models in a differentiable manner allows us to improve PSNR over baselines. Our module is an easy-to-use plugin that can be applied to NeRF variants to improve performance. The code and data are currently available at https://github.com/POSTECH-CVLab/SCNeRF.

• The paper introduces a novel algorithm that simultaneously calibrates cameras and learns 3D scene geometry without relying on pre-calibrated parameters.
• It integrates traditional pinhole models with non-linear distortion models and employs a projected ray distance loss to ensure geometric consistency.
• The approach significantly boosts PSNR in NeRF reconstructions, offering promising applications in VR, robotics, and autonomous navigation.

Analysis of Self-Calibrating Neural Radiance Fields

"Self-Calibrating Neural Radiance Fields" introduces a novel approach for the joint learning of camera calibration and 3D scene geometry using neural radiance fields (NeRFs). This work significantly contributes to reducing the dependency on pre-calibrated camera parameters, a typical prerequisite for many three-dimensional reconstructions and view synthesis tasks.

Core Contributions

The paper proposes an innovative camera self-calibration algorithm that can handle generic cameras with complex, non-linear distortions. The approach uniquely combines the calibration of camera parameters while simultaneously learning the 3D geometry of the scene without requiring any external calibration objects. The presented model integrates traditional pinhole camera models with radial distortion and a generic non-linear distortion model. Such a comprehensive model accounts for the intricate distortions common in real-world lenses, which existing self-calibration methods often overlook due to their linear assumptions.

A distinct aspect of the method is the inclusion of a new geometric loss function called the projected ray distance loss. This helps ensure geometric consistency even with complex non-linear camera models, which is a departure from traditional reliance on less robust geometric constraints.

Numerical Validation and Efficiency

The authors validated their algorithm against standard real image datasets. They demonstrated that their method could independently acquire camera intrinsic and extrinsic parameters from scratch without relying on initializations like COLMAP. Furthermore, upon integration of this self-calibration model as a plugin to NeRF variants, the approach improved the Peak Signal-to-Noise Ratio (PSNR) over baseline methods significantly.

Implications and Future Scope

The implications of this research are substantial for fields requiring high-fidelity 3D reconstructions and novel view synthesis, including virtual and augmented reality applications, robotics, and autonomous navigation systems. Its ability to enhance NeRFs by incorporating accurate camera models in a differentiable manner opens opportunities for further refinement of machine learning-based approaches in photogrammetry and computer vision, especially in scenarios where pre-calibrated cameras are unavailable or impractical.

Theoretically, this work suggests fertile ground for future exploration into more intricate distortion models in computer vision tasks and enhancing the robustness and applicability of neural scene representations. The proposed framework could be an antecedent to more generalized models that cater to a broader array of imaging conditions and devices.

Conclusion

This paper exemplifies a meaningful advancement in the automation and accuracy of self-calibration for neural radiance fields, effectively bridging a gap between traditional camera models and the nuances of real-world optics. By introducing an efficient differentiable framework, the authors provide a basis for applying neural representations in complex scenarios, suggesting an optimistic trajectory for future research in autonomous and vision-based systems.