# Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey arXiv: https://arxiv.org/abs/2308.15733 · HTML: https://mlsanity.com/paper/2308.15733/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 49 references that are in the map. - First posted on arXiv 30 Aug 2023. ## Builds on (most cited first, 30 of 30) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022) - [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021) - [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021) - [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020) - [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021) - [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020) - [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022) - [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021) - [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021) - [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022) - [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021) - [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021) - [Neural Scene Graphs for Dynamic Scenes](https://mlsanity.com/paper/2011.10379/) (2020) - [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021) - [Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering](https://mlsanity.com/paper/2109.01847/) (2021) - [Editing Conditional Radiance Fields](https://mlsanity.com/paper/2105.06466/) (2021) - [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022) - [NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing](https://mlsanity.com/paper/2207.11911/) (2022) - [RigNeRF: Fully Controllable Neural 3D Portraits](https://mlsanity.com/paper/2206.06481/) (2022) - [HDR-NeRF: High Dynamic Range Neural Radiance Fields](https://mlsanity.com/paper/2111.14451/) (2021) - [NerfAcc: A General NeRF Acceleration Toolbox](https://mlsanity.com/paper/2210.04847/) (2022) - [Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation](https://mlsanity.com/paper/2202.13162/) (2022) - [Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras](https://mlsanity.com/paper/2203.08896/) (2022) - [DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering](https://mlsanity.com/paper/2106.03798/) (2021) - [Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis](https://mlsanity.com/paper/2201.01683/) (2022) - [HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields](https://mlsanity.com/paper/2208.06787/) (2022) - [RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo](https://mlsanity.com/paper/2307.10233/) (2023) - [Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light](https://mlsanity.com/paper/2203.10493/) (2022) - [Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV](https://mlsanity.com/paper/1712.07122/) (2017) ## Built on by (most cited first, 11 of 11) - [CityGS-X: A Scalable Architecture for Efficient and Geometrically Accurate Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2503.23044/) (2025) - [DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery](https://mlsanity.com/paper/2503.16964/) (2025) - [Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks](https://mlsanity.com/paper/2406.15149/) (2024) - [Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning](https://mlsanity.com/paper/2403.11460/) (2024) - [UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception](https://mlsanity.com/paper/2310.16255/) (2023) - [AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal](https://mlsanity.com/paper/2502.16351/) (2025) - [DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding](https://mlsanity.com/paper/2403.11776/) (2024) - [DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes](https://mlsanity.com/paper/2411.12309/) (2024) - [NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery](https://mlsanity.com/paper/2410.23901/) (2024) - [No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation](https://mlsanity.com/paper/2607.19288/) (2026) - [AdaAnchor4D: Anchor-Conditioned Spatiotemporal Feature Aggregation for Monocular UAV 4D Reconstruction](https://mlsanity.com/paper/2607.28320/) (2026) ## Abstract (arXiv) 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.