Neural rendering has garnered substantial attention owing to its capacity for creating realistic 3D scenes. However, its applicability to extensive scenes remains challenging, with limitations in effectiveness. In this work, we propose the Drone-NeRF framework to enhance the efficient reconstruction of unbounded large-scale scenes suited for drone oblique photography using Neural Radiance Fields (NeRF). Our approach involves dividing the scene into uniform sub-blocks based on camera position and depth visibility. Sub-scenes are trained in parallel using NeRF, then merged for a complete scene. We refine the model by optimizing camera poses and guiding NeRF with a uniform sampler. Integrating chosen samples enhances accuracy. A hash-coded fusion MLP accelerates density representation, yielding RGB and Depth outputs. Our framework accounts for sub-scene constraints, reduces parallel-training noise, handles shadow occlusion, and merges sub-regions for a polished rendering result. This Drone-NeRF framework demonstrates promising capabilities in addressing challenges related to scene complexity, rendering efficiency, and accuracy in drone-obtained imagery.

翻译：神经渲染因其创建逼真三维场景的能力而备受关注。然而，其在大规模场景中的适用性仍面临挑战，有效性受限。在本工作中，我们提出Drone-NeRF框架，以增强基于神经辐射场（NeRF）的、适用于无人机倾斜摄影的无限大规模场景的高效重建。我们的方法根据相机位置和深度可见性将场景划分为均匀子块。子场景使用NeRF并行训练，随后合并以形成完整场景。我们通过优化相机姿态并引入均匀采样器引导NeRF来改进模型，整合所选样本以提升精度。通过哈希编码融合多层感知机（MLP）加速密度表示，从而输出RGB和深度信息。该框架考虑了子场景约束，降低了并行训练噪声，处理了阴影遮挡，并融合子区域以获得精细渲染结果。Drone-NeRF框架在应对无人机图像中的场景复杂度、渲染效率及精度挑战方面展示了良好能力。