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.

翻译：尽管三维城市数据集的可用性日益增长，但由于计算瓶颈以及与数据交互的复杂性，从中提取洞见仍然具有挑战性。事实上，三维城市环境的复杂几何结构导致了高度的遮挡，并需要大量手动视点调整，这使得大规模探索效率低下。为解决此问题，我们提出了一种用于三维数据探索的基于视点的方法，其中向量场编码了来自环境的视点。为支持此方法，我们引入了一种基于神经场的方法，该方法构建了三维环境的高效隐式表示。这种表示既支持更快的直接查询（包括视点评估指标的计算），也支持逆向查询，有助于避免遮挡并促进搜索符合期望数据模式的视点。我们的方法支持关键的城市场景分析任务，如可见性评估、日照暴露评估以及新开发项目的视觉影响评估。我们通过定量实验、基于真实世界城市挑战的案例研究以及领域专家的反馈来验证我们的方法。结果表明，该方法在寻找理想视点、分析建筑立面可见性以及评估户外空间视点方面具有有效性。代码和数据公开于 https://urbantk.org/neural-3d。