publications

2025

1. CVPR 2025

   

   Parametric Point Cloud Completion for Polygonal Surface Reconstruction

   Zhaiyu Chen, Yuqing Wang, Liangliang Nan, and 1 more author

   IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

   Abs Bib Code Website

   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.

   @article{chen2025paco,
     title = {Parametric Point Cloud Completion for Polygonal Surface Reconstruction},
     journal = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     year = {2025},
     author = {Chen, Zhaiyu and Wang, Yuqing and Nan, Liangliang and Zhu, Xiao Xiang},
   }

2024

1. ISPRS 2024

   

   PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds

   Zhaiyu Chen, Yilei Shi, Liangliang Nan, and 2 more authors

   ISPRS Journal of Photogrammetry and Remote Sensing, 2024

   Abs DOI Bib HTML Code

   We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposition via graph node classification, achieving a watertight and compact reconstruction. To effectively represent arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based sampling strategy to generate polyhedron-wise queries. These queries are then incorporated with inter-polyhedron adjacency to enhance the classification. PolyGNN is end-to-end optimizable and is designed to accommodate variable-size input points, polyhedra, and queries with an index-driven batching technique. To address the abstraction gap between existing city-building models and the underlying instances, and provide a fair evaluation of the proposed method, we develop our method on a large-scale synthetic dataset with well-defined ground truths of polyhedral labels. We further conduct a transferability analysis across cities and on real-world point clouds. Both qualitative and quantitative results demonstrate the effectiveness of our method, particularly its efficiency for large-scale reconstructions.

   @article{chen2024polygnn,
     title = {PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds},
     journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
     volume = {218},
     pages = {693-706},
     year = {2024},
     issn = {0924-2716},
     doi = {10.1016/j.isprsjprs.2024.09.031},
     author = {Chen, Zhaiyu and Shi, Yilei and Nan, Liangliang and Xiong, Zhitong and Zhu, Xiao Xiang},
   }

2. GRSM 2024

   

   Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation

   Shan Zhao, Zhaiyu Chen, Zhitong Xiong, and 3 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2024

   Abs DOI Bib HTML

   Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs’ applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies.

   @article{zhao2024gnn,
     author = {Zhao, Shan and Chen, Zhaiyu and Xiong, Zhitong and Shi, Yilei and Saha, Sudipan and Zhu, Xiao Xiang},
     journal = {IEEE Geoscience and Remote Sensing Magazine},
     title = {Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation},
     year = {2024},
     volume = {},
     number = {},
     pages = {2-35},
     keywords = {Earth;Data models;Graph neural networks;Monitoring;Meteorology;Reviews;Time series analysis;Point cloud compression;Context modeling;Training},
     doi = {10.1109/MGRS.2024.3493972},
   }

3. SIGSPATIAL 2024

   

   Walking in the Shade: Shadow-oriented Navigation for Pedestrians

   Yu Feng, Puzhen Zhang, Jiaying Xue, and 2 more authors

   In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, Atlanta, GA, USA, 2024

   Abs DOI Bib HTML

   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.

   @inproceedings{feng2024shade,
     author = {Feng, Yu and Zhang, Puzhen and Xue, Jiaying and Chen, Zhaiyu and Meng, Liqiu},
     title = {Walking in the Shade: Shadow-oriented Navigation for Pedestrians},
     year = {2024},
     isbn = {9798400711077},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3678717.3691287},
     booktitle = {Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems},
     pages = {677–680},
     numpages = {4},
     keywords = {3D city model, Comfort-oriented navigation, Pedestrian navigation, Shadow simulation},
     location = {Atlanta, GA, USA},
     series = {SIGSPATIAL '24},
   }

2022

1. ISPRS 2022

   

   Reconstructing Compact Building Models from Point Clouds using Deep Implicit Fields

   Zhaiyu Chen, Hugo Ledoux, Seyran Khademi, and 1 more author

   ISPRS Journal of Photogrammetry and Remote Sensing, 2022

   Abs DOI Bib HTML Code

   While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing compact, watertight, polygonal building models from point clouds. Our framework comprises three components: (a) a cell complex is generated via adaptive space partitioning that provides a polyhedral embedding as the candidate set; (b) an implicit field is learned by a deep neural network that facilitates building occupancy estimation; (c) a Markov random field is formulated to extract the outer surface of a building via combinatorial optimization. We evaluate and compare our method with state-of-the-art methods in generic reconstruction, model-based reconstruction, geometry simplification, and primitive assembly. Experiments on both synthetic and real-world point clouds have demonstrated that, with our neural-guided strategy, high-quality building models can be obtained with significant advantages in fidelity, compactness, and computational efficiency. Our method also shows robustness to noise and insufficient measurements, and it can directly generalize from synthetic scans to real-world measurements.

   @article{chen2022points2poly,
     title = {Reconstructing Compact Building Models from Point Clouds using Deep Implicit Fields},
     journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
     volume = {194},
     pages = {58-73},
     year = {2022},
     issn = {0924-2716},
     doi = {10.1016/j.isprsjprs.2022.09.017},
     author = {Chen, Zhaiyu and Ledoux, Hugo and Khademi, Seyran and Nan, Liangliang},
   }