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.

我们提出了 Drone-NeRF 框架，通过使用神经辐射场（NeRF）来增强适用于无人机倾斜摄影的无界大规模场景的高效重建，将场景根据摄像机位置和深度可见性划分为均匀的子块，在 NeRF 的并行训练中训练子场景，然后合并为完整场景，通过优化相机姿态和引导 NeRF 使用均匀采样器来完善模型，集成选择的样本提高准确性，哈希编码融合 MLP 加速密度表示，生成 RGB 和深度输出，该框架解决了与场景复杂性、渲染效率和无人机获取图像准确性相关的挑战，展示了有前景的能力。