IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs
<|reference_start|>IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs: Neural Radiance Fields (NeRF) have recently demonstrated significant efficiency in the reconstruction of three-dimensional scenes and the synthesis of novel perspectives from a limited set of two-dimensional images. However, large-scale reconstruction using NeRF requires a substantial amount of aerial imagery for training, making it impractical in resource-constrained environments. This paper introduces an innovative incremental optimal view selection framework, IOVS4NeRF, designed to model a 3D scene within a restricted input budget. Specifically, our approach involves adding the existing training set with newly acquired samples, guided by a computed novel hybrid uncertainty of candidate views, which integrates rendering uncertainty and positional uncertainty. By selecting views that offer the highest information gain, the quality of novel view synthesis can be enhanced with minimal additional resources. Comprehensive experiments substantiate the efficiency of our model in realistic scenes, outperforming baselines and similar prior works, particularly under conditions of sparse training data.<|reference_end|>
@article{xie2024iovs4nerf:incremental,
    title={IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs},
    author={Jingpeng Xie, Shiyu Tan, Yuanlei Wang, Yizhen Lao},
    journal={arXiv preprint arXiv:2407.18611},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18611},
    primaryClass={cs.CV}
}