  Neural Radiance Fields (NeRF) have shown remarkable performance in learning
3D scenes. However, NeRF exhibits vulnerability when confronted with
distractors in the training images -- unexpected objects are present only
within specific views, such as moving entities like pedestrians or birds.
Excluding distractors during dataset construction is a straightforward
solution, but without prior knowledge of their types and quantities, it becomes
prohibitively expensive. In this paper, we propose PruNeRF, a segment-centric
dataset pruning framework via 3D spatial consistency, that effectively
identifies and prunes the distractors. We first examine existing metrics for
measuring pixel-wise distraction and introduce Influence Functions for more
accurate measurements. Then, we assess 3D spatial consistency using a
depth-based reprojection technique to obtain 3D-aware distraction. Furthermore,
we incorporate segmentation for pixel-to-segment refinement, enabling more
precise identification. Our experiments on benchmark datasets demonstrate that
PruNeRF consistently outperforms state-of-the-art methods in robustness against
distractors.
