nerf2nerf: Pairwise Registration of Neural Radiance Fields (2211.01600v1)

Abstract: We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained from collections of calibrated images. NeRF does not decompose illumination and color, so to make registration invariant to illumination, we introduce the concept of a ''surface field'' -- a field distilled from a pre-trained NeRF model that measures the likelihood of a point being on the surface of an object. We then cast nerf2nerf registration as a robust optimization that iteratively seeks a rigid transformation that aligns the surface fields of the two scenes. We evaluate the effectiveness of our technique by introducing a dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative evaluations and comparisons to classical registration techniques, while our real scenes demonstrate the validity of our technique in real-world scenarios. Additional results available at: https://nerf2nerf.github.io

• The paper introduces an ICP-inspired pairwise registration method that directly aligns Neural Radiance Fields, bypassing classical geometric conversions.
• It formulates the task as an optimization problem that minimizes transformation error using keypoint guided surface field matching.
• Evaluations demonstrate significant improvements in translational and rotational accuracy for 3D scene reconstruction in synthetic and real-world datasets.

An Overview of "nerf2nerf: Pairwise Registration of Neural Radiance Fields"

The paper "nerf2nerf: Pairwise Registration of Neural Radiance Fields," offers a methodological advancement in the registration of neural fields, particularly focusing on Neural Radiance Fields (NeRFs). This paper is situated within the current trend of utilizing neural fields as a 3D representation paradigm, which substantially enhances 3D scene reconstruction through differentiable volume rendering techniques. The novel contribution of this work is a method for pairwise registration directly on NeRFs, avoiding conversion to classical geometric forms such as point clouds or meshes.

Core Contributions

The proposed nerf2nerf method extends classical iterative closest point (ICP) techniques to the field of NeRFs. This adaptation facilitates the alignment of partially overlapping 3D neural scenes. The researchers introduce the concept of a "surface field," a geometric representation derived from a NeRF that remains invariant under different lighting conditions. This is crucial because NeRFs inherently do not decompose illumination and color, presenting a challenge for direct radiance-based registration.

Methodological Insights

The registration process is formulated as an optimization problem that seeks to minimize the transformation error between the surface fields of two NeRFs. To achieve robust optimization, the technique employs a two-term energy function combining both surface matching and initial keypoint alignment, gradually transitioning between these terms through an annealing process. The keypoint annotations by human operators assist in constraining the solution space initially, while the surface field optimization refines the alignment to a precise registration.

Furthermore, the paper addresses the challenge of sampling within the NeRF framework, using a Metropolis-Hastings strategy to efficiently sample the 3D space, thereby improving the computational feasibility of the registration process.

Evaluation and Results

The effectiveness of nerf2nerf is demonstrated through quantitative evaluations on a synthetic dataset of pre-trained NeRF scenes, showcasing its superiority over traditional methods that rely on explicit conversions. The results indicate significant improvements in both translational and rotational components of alignment, as well as in a modified 3D-ADD metric, which assesses the alignment accuracy. Additionally, qualitative results on real-world NeRF scene registrations underline its applicability in practical scenarios.

Implications and Future Directions

The implications of the nerf2nerf methodology are substantial for applications within computer vision and robotics, where precise 3D alignment is fundamental. The method's capability to register scenes under varying lighting conditions without reliance on explicit conversions opens new pathways for neural field integration in large-scale visual SLAM systems and potentially in dynamic scene understanding.

Future developments could explore extending this approach to handle more complex scene dynamics, integrating learned features to automate keypoint selection, or enhancing the robustness of sampling techniques. Furthermore, improvements in neural field representation efficiency might enable real-time registration capabilities, enhancing the viability of this approach for live applications.

In summary, the paper contributes a solid foundation for extending classical geometric processing techniques into the domain of neural fields, situating itself as a critical step toward harnessing the full potential of NeRF and allied technologies in both static and dynamic environments.