Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked. To address this gap, we introduce parametric completion, a novel paradigm for point cloud completion, which recovers parametric primitives instead of individual points to convey high-level geometric structures. Our presented approach, PaCo, enables high-quality polygonal surface reconstruction by leveraging plane proxies that encapsulate both plane parameters and inlier points, proving particularly effective in challenging scenarios with highly incomplete data. Comprehensive evaluations of our approach on the ABC dataset establish its effectiveness with superior performance and set a new standard for polygonal surface reconstruction from incomplete data. Project page: https://parametric-completion.github.io.

3D shape completion methods typically assume scans are pre-aligned to a canonical frame. This leaks pose and scale cues that networks may exploit to memorize absolute positions rather than inferring intrinsic geometry. When such alignment is absent in real data, performance collapses. We argue that robust generalization demands architectural equivariance to the similarity group, SIM(3), so the model remains agnostic to pose and scale. Following this principle, we introduce the first SIM(3)-equivariant shape completion network, whose modular layers successively canonicalize features, reason over similarity-invariant geometry, and restore the original frame. Under a de-biased evaluation protocol that removes the hidden cues, our model outperforms both equivariant and augmentation baselines on the PCN benchmark. It also sets new cross-domain records on real driving and indoor scans, lowering minimal matching distance on KITTI by 17\% and Chamfer distance $\ell1$ on OmniObject3D by 14\%. Perhaps surprisingly, ours under the stricter protocol still outperforms competitors under their biased settings. These results establish full SIM(3) equivariance as an effective route to truly generalizable shape completion.

Excessive exposure to the sun and the resulting heat poses health risks to pedestrians in hot weather. To mitigate these risks, we propose a shadow-oriented navigation system that offers cooler and more convenient walking routes by simulating shadows. Our system integrates a manually corrected pedestrian network from OpenStreetMap with LoD2 3D city models, using a ray tracing module for real-time shadow simulation. It optimizes routes to be either cooler or shorter based on user preferences, with easy verification through 3D scene visualization. Our navigation system has been implemented in a study area in Munich, Germany, with further discussions on the technical feasibility and challenges of extending it to larger areas.

Non-intrusive estimation of building energy efficiency has profound applications in advancing sustainability in the built environment. Recent studies often focus on predicting energy performance alone, neglecting the interplay between the performance and related building semantics. This paper investigates whether incorporating semantic attributes benefits energy efficiency estimation. We develop a neural network to estimate energy efficiency, with building age and usage type as additional supervision for multi-task learning. The neural network processes both aerial imagery and airborne LiDAR data to classify buildings as energy-efficient or inefficient. Our results demonstrate the effectiveness of the superimposed semantics, particularly with building age. With the multi-task model achieving a 63.78% F1 score and outperforming that supervised solely with energy efficiency by 2.86%, this paper reveals the potential of integrating semantic attributes in modeling building energy performance.

Recognizing building attributes from remote sensing images is crucial for various applications. Recent developments in deep learning have demonstrated promising results in identifying these attributes. Nonetheless, a major challenge is the requirement for extensive and accurate building attribute data. Two primary data sources are commonly considered: Open-StreetMap (OSM), which offers global building information but often lacks completeness and correctness, and cadastral data, known for its high quality but typically restricted to certain areas. These two sources enable comparison between deep learning models trained on noisy and incomplete OSM data and those trained on accurate and complete cadastral data. In this work, comprehensive experiments on buildings in Bavaria, Germany, are conducted, covering diverse attributes such as footprints, use, and height. A large building dataset with corresponding building attribute labels from OSM and cadastral data is created, with OSM data featuring varying levels of incompleteness and noise for different attributes and cadastral data serving as ground truth. Moreover, we evaluate the effectiveness of several prevailing methods designed to handle noisy and incomplete labels, assessing their applicability to real-world scenarios with incomplete and noisy OSM labels.

Satellites offer a unique, comprehensive viewpoint for information retrieval from conflict zones worldwide. This study leverages medium-resolution (GSD 3 m) satellite imagery from the commercial provider Planet Labs to analyze the immediate infrastructural changes resulting from the recent conflict in the Gaza Strip, starting in October 2023. Utilizing an empirical model, we detect and quantify these changes, providing a detailed assessment of the conflict’s impact on the region’s infrastructure. The results of our investigation are evaluated, revealing its reliability in change detection in the affected area. This paper underscores the critical role of satellite imagery in conflict analysis and offers insights for future humanitarian and reconstruction efforts.

Three-dimensional (3D) building models play a crucial role in shaping digital twin cities and enabling a wide range of urban applications. However, one challenge remains in obtaining a compact representation of buildings from remote sensing. This paper introduces a novel deep learning approach to reconstructing polygonal building models from LiDAR point clouds. Our method leverages a graph neural network to assemble the polyhedra generated through space partitioning, thereby formulating building surface reconstruction as a graph node classification problem. To facilitate network training, we construct a synthetic dataset by simulating aerial LiDAR point clouds on building surface meshes. Experimental results demonstrate the effectiveness of our method, achieving a polyhedral classification accuracy of 96.4%. Moreover, our approach offers high efficiency and interpretability through end-to-end optimization.