A Neural Field-Based Approach for View Computation & Data Exploration in 3D Urban Environments
This work addresses a domain-specific problem for urban planners and analysts by improving efficiency in 3D data exploration, though it appears incremental as it builds on neural field techniques for a specific application.
The paper tackles the challenge of inefficient data exploration in 3D urban environments due to occlusion and manual viewpoint adjustments by proposing a neural field-based method that encodes views in a vector field, enabling faster queries and inverse searches for desirable viewpoints, with validation showing effectiveness in tasks like visibility assessments and solar exposure evaluation.
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.