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

Understand

Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals.

• In this paper, we address the problem of positioning an RGB camera to collect the most informative images to represent an unknown scene, given a limited measurement budget.
• We propose a novel mapless planning framework to iteratively plan the next best camera view based on collected image measurements.
• A key aspect of our approach is a new technique for uncertainty estimation in image-based neural rendering, which guides measurement acquisition at the most uncertain view among view candidates, thus maximising the information value during data collection.