# A Neural Field-Based Approach for View Computation & Data Exploration in 3D Urban Environments

arXiv: https://arxiv.org/abs/2511.14742 · HTML: https://mlsanity.com/paper/2511.14742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020)
- [Compressive Neural Representations of Volumetric Scalar Fields](https://mlsanity.com/paper/2104.04523/) (2021)
- [Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies](https://mlsanity.com/paper/2304.06872/) (2023)
- [VoxCity: A Seamless Framework for Open Geospatial Data Integration, Grid-Based Semantic 3D City Model Generation, and Urban Environment Simulation](https://mlsanity.com/paper/2504.13934/) (2025)
- [The State of the Art in Visual Analytics for 3D Urban Data](https://mlsanity.com/paper/2404.15976/) (2024)
- [Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics](https://mlsanity.com/paper/2408.06139/) (2024)
- [Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes](https://mlsanity.com/paper/1704.02393/) (2017)
- [UrbanRama: Navigating Cities in Virtual Reality](https://mlsanity.com/paper/2112.06082/) (2021)
- [A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics](https://mlsanity.com/paper/2208.05370/) (2022)

## Abstract (arXiv)

Despite the growing availability of 3D urban datasets, extracting insights remains challenging due to computational bottlenecks and the complexity of interacting with data. In fact, the intricate geometry of 3D urban environments results in high degrees of occlusion and requires extensive manual viewpoint adjustments that make large-scale exploration inefficient. To address this, we propose a view-based approach for 3D data exploration, where a vector field encodes views from the environment. To support this approach, we introduce a neural field-based method that constructs an efficient implicit representation of 3D environments. This representation enables both faster direct queries, which consist of the computation of view assessment indices, and inverse queries, which help avoid occlusion and facilitate the search for views that match desired data patterns. Our approach supports key urban analysis tasks such as visibility assessments, solar exposure evaluation, and assessing the visual impact of new developments. We validate our method through quantitative experiments, case studies informed by real-world urban challenges, and feedback from domain experts. Results show its effectiveness in finding desirable viewpoints, analyzing building facade visibility, and evaluating views from outdoor spaces. Code and data are publicly available at https://urbantk.org/neural-3d.