NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering

Abstract-- Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. By incrementally adding new measurements into our image collection, our approach efficiently explores an unknown scene in a mapless manner. Our planning experiments using synthetic and real-world data verify that our uncertainty-guided Figure 1: Our novel NBV planning framework exploits uncertainty approach finds informative images leading to more accurate estimation in image-based neural rendering to guide measurement scene representations when compared against baselines. Brighter frustums indicate higher average uncertainty from the view. While In this work, we present a new framework for iteratively showing promising results, these studies follow an active planning the next best view (NBV) for an RGB camera to learning [15] paradigm to collect the most informative, i.e. explore an unknown scene. Given a limited measurement most uncertain, images for periodically re-training a NeRF budget, our goal is to actively position the sensor to gather to improve the scene representation with minimal data.