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Neural Radiance Fields (NeRFs) are gaining significant interest for onlineactive object reconstruction due to their exceptional memory efficiency andrequirement for only posed RGB inputs. Previous NeRF-based view planningmethods exhibit computational inefficiency since they rely on an iterativeparadigm, 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, wepropose a non-iterative pipeline based on the Prediction of the Required numberof Views (PRV). The key idea behind our approach is that the required number ofviews to reconstruct an object depends on its complexity. Therefore, we designa deep neural network, named PRVNet, to predict the required number of views,allowing us to tailor the data acquisition based on the object complexity andplan a globally shortest path. To train our PRVNet, we generate supervisionlabels using the ShapeNet dataset. Simulated experiments show that ourPRV-based view planning method outperforms baselines, achieving goodreconstruction quality while significantly reducing movement cost and planningtime. We further justify the generalization ability of our approach in areal-world experiment.

How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?