Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale
  Drone Survey
Zhihao Jia Bing Wang Changhao Chen
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.