AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height Large-scale Outdoor Scene Rendering
<|reference_start|>AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height Large-scale Outdoor Scene Rendering: Existing neural radiance fields (NeRF)-based novel view synthesis methods for large-scale outdoor scenes are mainly built on a single altitude. Moreover, they often require a priori camera shooting height and scene scope, leading to inefficient and impractical applications when camera altitude changes. In this work, we propose an end-to-end framework, termed AG-NeRF, and seek to reduce the training cost of building good reconstructions by synthesizing free-viewpoint images based on varying altitudes of scenes. Specifically, to tackle the detail variation problem from low altitude (drone-level) to high altitude (satellite-level), a source image selection method and an attention-based feature fusion approach are developed to extract and fuse the most relevant features of target view from multi-height images for high-fidelity rendering. Extensive experiments demonstrate that AG-NeRF achieves SOTA performance on 56 Leonard and Transamerica benchmarks and only requires a half hour of training time to reach the competitive PSNR as compared to the latest BungeeNeRF.<|reference_end|>
@article{guo2024ag-nerf:,
    title={AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height
  Large-scale Outdoor Scene Rendering},
    author={Jingfeng Guo, Xiaohan Zhang, Baozhu Zhao, Qi Liu},
    journal={arXiv preprint arXiv:2404.11897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11897},
    primaryClass={cs.CV}
}