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

2025-11-18

AI Summary

Urban visibility analysis in 3D city environments suffers from low computational efficiency and labor-intensive viewpoint adjustments due to geometric complexity and severe occlusion. To address this, we propose the first integration of neural radiance fields (NeRF) into urban view modeling, establishing an implicit spatial representation of cities. We further augment this representation with vector-field encoding of viewpoint parameters, enabling efficient forward queries (e.g., solar access, field-of-view assessment) and inverse queries (e.g., optimal viewpoint search, visual impact inversion). Unlike conventional mesh-based approaches, our method avoids explicit surface reconstruction, significantly reducing computational overhead. Evaluated on façade visibility and public-space visual quality assessment, it demonstrates high automation, sub-second interactive response, and superior analytical accuracy. This work establishes a novel data-driven paradigm for large-scale urban scene exploration.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Addressing computational bottlenecks in 3D urban data exploration
• Solving occlusion issues in complex 3D urban environment geometry
• Enabling efficient viewpoint search for urban analysis tasks

Innovation

• Neural field-based implicit representation for 3D environments
• Vector field encoding views for data exploration
• Direct and inverse queries for view assessment