PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
<|reference_start|>PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency: 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.<|reference_end|>
@article{jung2024prunerf:,
    title={PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency},
    author={Yeonsung Jung, Heecheol Yun, Joonhyung Park, Jin-Hwa Kim, Eunho Yang},
    journal={arXiv preprint arXiv:2406.00798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00798},
    primaryClass={cs.CV cs.AI}
}