[Paper Review] Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields

Soomin Lee, Le Chen+4 more|arXiv (Cornell University)|Sep 17, 2022

Advanced Vision and Imaging4 citations

TL;DR

This paper proposes an uncertainty-guided next-best-view policy for active robotic 3D reconstruction using neural radiance fields (NeRF). By computing the entropy of weight distributions along rays in the NeRF representation, the method estimates volumetric uncertainty and guides view selection to improve reconstruction quality, achieving state-of-the-art results on synthetic and real-world benchmarks with fewer views.

ABSTRACT

In this paper, we tackle the problem of active robotic 3D reconstruction of an object. In particular, we study how a mobile robot with an arm-held camera can select a favorable number of views to recover an object's 3D shape efficiently. Contrary to the existing solution to this problem, we leverage the popular neural radiance fields-based object representation, which has recently shown impressive results for various computer vision tasks. However, it is not straightforward to directly reason about an object's explicit 3D geometric details using such a representation, making the next-best-view selection problem for dense 3D reconstruction challenging. This paper introduces a ray-based volumetric uncertainty estimator, which computes the entropy of the weight distribution of the color samples along each ray of the object's implicit neural representation. We show that it is possible to infer the uncertainty of the underlying 3D geometry given a novel view with the proposed estimator. We then present a next-best-view selection policy guided by the ray-based volumetric uncertainty in neural radiance fields-based representations. Encouraging experimental results on synthetic and real-world data suggest that the approach presented in this paper can enable a new research direction of using an implicit 3D object representation for the next-best-view problem in robot vision applications, distinguishing our approach from the existing approaches that rely on explicit 3D geometric modeling.

Motivation & Objective

• Address the challenge of active 3D reconstruction in robotic systems where camera pose selection must maximize information gain.
• Overcome limitations of existing methods that rely on explicit 3D representations like point clouds or voxels, which suffer from noise, occlusions, and reconstruction artifacts.
• Enable efficient and accurate dense 3D reconstruction using implicit neural representations such as NeRF, which offer high-fidelity geometry and appearance modeling.
• Develop a generalizable uncertainty estimation method applicable to various NeRF-based architectures for active vision tasks.

Proposed method

• Propose a ray-based volumetric uncertainty estimator that computes the entropy of the weight distribution of color samples along each ray in the NeRF representation.
• Use the estimated uncertainty as a proxy for geometric ambiguity, enabling identification of regions with high uncertainty that require further observation.
• Design a next-best-view selection policy that prioritizes views with high uncertainty while applying region clustering to avoid redundant, localized view choices.
• Integrate the uncertainty-guided policy into an iterative reconstruction pipeline, where NeRF is retrained after each new view is acquired.
• Demonstrate model-agnostic applicability by validating the method on both standard NeRF and faster TensoRF, showing consistent performance gains.
• Leverage the uncertainty estimator for dynamic view planning, ensuring progressive improvement in 3D reconstruction quality with minimal view count.

Experimental results

Research questions

• RQ1 Can uncertainty in the weight distribution of NeRF rays serve as a reliable proxy for geometric uncertainty in implicit 3D representations?
• RQ2 How effective is an uncertainty-guided policy in selecting informative next-best-views for active 3D reconstruction compared to random, heuristic, or similarity-based baselines?
• RQ3 To what extent can the proposed method reduce the number of required views while maintaining or improving reconstruction quality compared to full-view acquisition?
• RQ4 Is the uncertainty estimation method generalizable across different NeRF-based architectures, including faster variants like TensoRF?

Key findings

• The proposed uncertainty-guided policy achieved an F-score of 0.5078 on the Lego benchmark using only 54 images, outperforming random (0.4895), heuristic (0.4234), and similarity-based policies (0.4163).
• With just 54 images, the method produced 3D mesh quality comparable to that obtained using all 150 images, demonstrating high sample efficiency.
• The average mean entropy across pixels decreased from 1.748 (initial) to 0.790 after four iterations, confirming progressive reduction in uncertainty and improved reconstruction confidence.
• The uncertainty computation time was reduced from 5 minutes to 2.8 minutes when using TensoRF, showing compatibility with fast NeRF implementations.
• Abllation studies confirmed that region clustering significantly improves reconstruction quality by preventing view concentration and enabling coverage of occluded regions.
• The method generalizes across NeRF variants, as demonstrated by successful transfer to TensoRF with minimal retraining and consistent performance gains.