# SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition arXiv: https://arxiv.org/abs/2312.16215 ยท HTML: https://mlsanity.com/paper/2312.16215/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 59 references that are in the map. - First posted on arXiv 24 Dec 2023. ## Builds on (most cited first, 26 of 26) - [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021) - [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020) - [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022) - [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023) - [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021) - [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021) - [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020) - [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020) - [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021) - [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021) - [BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering](https://mlsanity.com/paper/2112.05504/) (2021) - [Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation](https://mlsanity.com/paper/2112.05124/) (2021) - [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021) - [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020) - [NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images](https://mlsanity.com/paper/2305.17398/) (2023) - [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021) - [NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes](https://mlsanity.com/paper/2111.13260/) (2021) - [3D Neural Scene Representations for Visuomotor Control](https://mlsanity.com/paper/2107.04004/) (2021) - [IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images](https://mlsanity.com/paper/2204.02232/) (2022) - [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020) - [Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras](https://mlsanity.com/paper/2203.08896/) (2022) - [Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry](https://mlsanity.com/paper/2104.09877/) (2021) - [S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint](https://mlsanity.com/paper/2210.08936/) (2022) - [ORCa: Glossy Objects as Radiance Field Cameras](https://mlsanity.com/paper/2212.04531/) (2022) - [Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching](https://mlsanity.com/paper/2109.11121/) (2021) - [Towards Learning Neural Representations from Shadows](https://mlsanity.com/paper/2203.15946/) (2022) ## Built on by (most cited first, 9 of 9) - [SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images](https://mlsanity.com/paper/2508.09479/) (2025) - [Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervision](https://mlsanity.com/paper/2502.08352/) (2025) - [S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications](https://mlsanity.com/paper/2504.06920/) (2025) - [BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling](https://mlsanity.com/paper/2409.12014/) (2024) - [Semantic Neural Radiance Fields for Multi-Date Satellite Data](https://mlsanity.com/paper/2502.16992/) (2025) - [Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation](https://mlsanity.com/paper/2507.01631/) (2025) - [Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery](https://mlsanity.com/paper/2411.02972/) (2024) - [SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery](https://mlsanity.com/paper/2603.18634/) (2026) - [SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery](https://mlsanity.com/paper/2603.21931/) (2026) ## Abstract (arXiv) 3D modeling from satellite imagery is essential in areas of environmental science, urban planning, agriculture, and disaster response. However, traditional 3D modeling techniques face unique challenges in the remote sensing context, including limited multi-view baselines over extensive regions, varying direct, ambient, and complex illumination conditions, and time-varying scene changes across captures. In this work, we introduce SUNDIAL, a comprehensive approach to 3D reconstruction of satellite imagery using neural radiance fields. We jointly learn satellite scene geometry, illumination components, and sun direction in this single-model approach, and propose a secondary shadow ray casting technique to 1) improve scene geometry using oblique sun angles to render shadows, 2) enable physically-based disentanglement of scene albedo and illumination, and 3) determine the components of illumination from direct, ambient (sky), and complex sources. To achieve this, we incorporate lighting cues and geometric priors from remote sensing literature in a neural rendering approach, modeling physical properties of satellite scenes such as shadows, scattered sky illumination, and complex illumination and shading of vegetation and water. We evaluate the performance of SUNDIAL against existing NeRF-based techniques for satellite scene modeling and demonstrate improved scene and lighting disentanglement, novel view and lighting rendering, and geometry and sun direction estimation on challenging scenes with small baselines, sparse inputs, and variable illumination.