  Neural Radiance Fields (NeRFs) are gaining significant interest for online
active object reconstruction due to their exceptional memory efficiency and
requirement for only posed RGB inputs. Previous NeRF-based view planning
methods exhibit computational inefficiency since they rely on an iterative
paradigm, consisting of (1) retraining the NeRF when new images arrive; and (2)
planning a path to the next best view only. To address these limitations, we
propose a non-iterative pipeline based on the Prediction of the Required number
of Views (PRV). The key idea behind our approach is that the required number of
views to reconstruct an object depends on its complexity. Therefore, we design
a deep neural network, named PRVNet, to predict the required number of views,
allowing us to tailor the data acquisition based on the object complexity and
plan a globally shortest path. To train our PRVNet, we generate supervision
labels using the ShapeNet dataset. Simulated experiments show that our
PRV-based view planning method outperforms baselines, achieving good
reconstruction quality while significantly reducing movement cost and planning
time. We further justify the generalization ability of our approach in a
real-world experiment.
