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

Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey