Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields (2402.09722v1)

Abstract: Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.

• The paper introduces Reg-NF, a novel technique that optimizes a bidirectional registration loss over signed distance functions to align implicit surfaces between neural fields.
• The method eliminates the need for human-annotated keypoints and manages varying scale factors, thereby improving initialization for robust 6-DoF transformations.
• Experimental results on the ONR dataset demonstrate significant improvements in 3D scene reconstruction and object registration compared to existing methods.

Efficient Registration of Implicit Surfaces within Neural Fields Using Reg-NF

Introduction

The exploration of Neural Fields (NFs) for representing 3D scenes has been gaining traction, particularly in the context of robotics applications where compact and continuous representations of geometry and appearance are crucial. Despite the advancements, the challenge of registering multiple neural fields -- a vital step for tasks such as localization, object pose estimation, and 3D reconstruction -- has seen limited exploration. The work presented herein introduces Reg-NF, an approach for the efficient registration of implicit surfaces within neural fields. This method is noteworthy for its ability to handle neural fields of different scale factors, bypassing the need for human-annotated keypoints for initialization.

Methodology

Reg-NF stands out by proposing a neural fields-based registration that optimizes for the relative six degrees of freedom (6-DoF) transformation between two arbitrary neural fields. Key methodologies include:

• Bidirectional Registration Loss: Incorporates a bidirectional optimization over the surface values of two SDFs (Signed Distance Functions), leveraging the continuous and differentiable nature of neural fields.
• Multi-View Surface Sampling: Utilizes a novel sampling technique that gathers data from multiple views to accurately represent the surface geometry of objects within the neural fields, providing a robust initialization for the registration process.
• Volumetric Signed Neural Field Utilization: By employing volumetric signed neural fields, Reg-NF benefits from accurate geometric representations, which are crucial for effective registration.

Experimental Design and Results

The research presents an exhaustive set of experiments and ablation studies conducted using the proposed ONR dataset, composed of high-fidelity simulated images. Reg-NF exhibited remarkable performance in registering objects within scenes, demonstrating a significant improvement over existing methods such as nerf2nerf. Notably, the approach can handle different scale factors and does not rely on human-annotated keypoints, which are often impractical in robotics applications.

The experiments underscored Reg-NF's versatility in scenarios involving object substitutions and instance replacements within scene NFs, showcasing its potential in improving scene representations and enabling more dynamic interactions within simulated environments for robotics training.

Theoretical and Practical Implications

Reg-NF's ability to accurately register multiple neural fields has significant implications:

• Robotic Applications: Enhances crucial tasks in robotics, such as object recognition, scene understanding, and dynamic interaction within continuously represented scenes.
• Data-Driven Simulations: Facilitates object instance replacement and scene variation, offering extensive possibilities for generating diverse training environments for robotic systems.
• Future Directions in NF Research: Opens avenues for further exploration into neural field applications, particularly in unconstrained environments where traditional explicit scene representations fall short.

Conclusion and Future Work

Reg-NF represents a pivotal advancement in the efficient registration of implicit surfaces within neural fields, particularly for robotics applications. By successfully addressing the challenges of scale and reliance on human-annotated keypoints, the method paves the way for more robust and versatile use of neural fields in representing and interacting with 3D scenes. Future research can explore extending Reg-NF's capabilities to more complex scenes and investigating its potential in real-world robotics applications, including autonomous navigation and manipulation in dynamically changing environments.