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Autonomous robotic tasks require actively perceiving the environment toachieve application-specific goals. In this paper, we address the problem ofpositioning an RGB camera to collect the most informative images to representan unknown scene, given a limited measurement budget. We propose a novelmapless planning framework to iteratively plan the next best camera view basedon collected image measurements. A key aspect of our approach is a newtechnique for uncertainty estimation in image-based neural rendering, whichguides measurement acquisition at the most uncertain view among viewcandidates, thus maximising the information value during data collection. Byincrementally adding new measurements into our image collection, our approachefficiently explores an unknown scene in a mapless manner. We show that ouruncertainty estimation is generalisable and valuable for view planning inunknown scenes. Our planning experiments using synthetic and real-world dataverify that our uncertainty-guided approach finds informative images leading tomore accurate scene representations when compared against baselines.

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