Unsupervised Learning of Robust RGB-D Registration Leveraging Neural Radiance Fields

Core Concepts

A novel frame-to-model optimization framework for unsupervised RGB-D registration that leverages neural radiance fields (NeRF) to enhance robustness against multi-view inconsistency factors.

Abstract

This paper proposes NeRF-UR, an unsupervised RGB-D registration framework that leverages neural radiance fields (NeRF) to overcome the limitations of existing frame-to-frame optimization methods. The key insights are: Instead of enforcing photometric and geometric consistency between two registered frames, NeRF-UR uses the NeRF as a global model of the scene and optimizes the poses by enforcing consistency between the input frames and the NeRF-rerendered frames. This design can better handle multi-view inconsistency factors such as lighting changes, geometry occlusion and reflective materials. To bootstrap the NeRF optimization, the authors create a synthetic dataset, Sim-RGBD, through photo-realistic simulation. They first train the registration model on Sim-RGBD with ground-truth poses, and then fine-tune it on real-world data in an unsupervised manner. This enables distilling the capability of feature extraction and registration from simulation to reality. Extensive experiments on ScanNet and 3DMatch datasets demonstrate that NeRF-UR outperforms state-of-the-art supervised and unsupervised RGB-D registration methods, especially in challenging scenarios with low overlap or severe lighting changes.

Stats

The registration model can achieve 97.2% rotation accuracy (5°), 84.2% translation accuracy (5cm), and 93.2% Chamfer distance accuracy (1mm) on the ScanNet dataset. Compared to the current state-of-the-art method PointMBF, NeRF-UR gains 2.6 percentage points in rotation accuracy, 3.2 percentage points in translation accuracy, and 1.9 percentage points in Chamfer distance accuracy on the 3DMatch dataset.

Quotes

"To overcome the reliance on annotated data in learning-based methods, the exploration of better strategies to extract information from unlabeled data for achieving unsupervised learning in RGB-D registration has gradually become a research focus." "Enforcing the photometric and geometric consistency between the NeRF rerendering and the input frames can better optimize the estimated poses than the frame-to-frame methods, which enhances the learning signal for the registration model."

Key Insights Distilled From

NeRF-Guided Unsupervised Learning of RGB-D Registration

by Zhinan Yu,Zh... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00507.pdf

Deeper Inquiries

How can the proposed NeRF-guided unsupervised learning framework be extended to other 3D vision tasks beyond registration, such as localization, reconstruction, or scene understanding

The NeRF-guided unsupervised learning framework proposed in the context can be extended to various other 3D vision tasks beyond registration, such as localization, reconstruction, and scene understanding. For localization tasks, the NeRF model can be utilized to create a global representation of the scene, enabling accurate localization of the camera or sensor within that environment. By optimizing the poses based on the consistency between the input data and the NeRF-rerendered frames, the framework can provide robust and accurate localization results. In terms of reconstruction, the NeRF model can be leveraged to reconstruct detailed 3D scenes from RGB-D data. By refining the poses and optimizing the scene representation simultaneously, the framework can enhance the quality and accuracy of the reconstructed 3D models. For scene understanding, the global contextual information provided by the NeRF model can aid in semantic segmentation, object recognition, and scene understanding tasks. By incorporating the scene representation from NeRF into deep learning models, the framework can improve the understanding of complex 3D scenes and objects. Overall, by adapting the NeRF-guided unsupervised learning framework to these tasks, it can enhance the performance and robustness of various 3D vision applications, leading to more accurate and reliable results.

What are the potential limitations of the NeRF-based approach, and how can they be addressed to further improve the robustness and generalization of the registration model

While the NeRF-based approach offers significant advantages in modeling complex scenes and optimizing camera poses, there are potential limitations that need to be addressed to further improve the robustness and generalization of the registration model: Computational Complexity: NeRF training and optimization can be computationally intensive, especially when dealing with large-scale scenes or datasets. Implementing optimization strategies to reduce computational overhead and improve efficiency is crucial. Limited Viewpoints: NeRF models may struggle with capturing scenes from all possible viewpoints, leading to incomplete or biased representations. Incorporating multi-view information or adaptive sampling techniques can help address this limitation. Generalization to Real-world Scenarios: While synthetic data bootstrapping is effective, ensuring the model generalizes well to real-world scenarios with diverse lighting conditions, textures, and occlusions is essential. Augmenting the synthetic dataset with more realistic and diverse scenes can help bridge the gap between simulation and reality. Noise and Outliers: NeRF optimization may be sensitive to noise and outliers in the input data, leading to suboptimal results. Robust optimization techniques and data preprocessing methods can help mitigate the impact of noise and outliers on the registration model. By addressing these limitations through advanced optimization strategies, data augmentation techniques, and robust modeling approaches, the NeRF-based framework can be further improved in terms of robustness and generalization.

Given the success of the synthetic data bootstrapping, how can we design more realistic and diverse synthetic datasets to better bridge the gap between simulation and real-world scenarios

To design more realistic and diverse synthetic datasets for better bridging the gap between simulation and real-world scenarios, several strategies can be employed: Texture and Material Variation: Introduce a wide range of textures, materials, and surface properties in the synthetic scenes to mimic real-world diversity. This can include different types of surfaces, reflective materials, and textures to enhance realism. Lighting Conditions: Incorporate varying lighting conditions, including different intensities, directions, and color temperatures, to simulate real-world lighting scenarios. This can help the model adapt to different lighting conditions and improve generalization. Object Placement and Interactions: Create scenes with realistic object placements, interactions, and occlusions to simulate complex real-world environments. This can help the model learn to handle occlusions and complex spatial relationships. Dynamic Elements: Introduce dynamic elements such as moving objects, changing lighting conditions, or dynamic scenes to simulate real-world dynamics. This can enhance the model's ability to handle dynamic environments. By incorporating these elements into the synthetic dataset generation process, the synthetic data can better reflect the complexities and variations present in real-world scenarios, ultimately improving the model's performance and generalization capabilities.