79 citations · 113 across the 9 of their papers we have counts for
9 papers
LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset
Matthias Kahl, Zhaiyu Chen, Sudipan Saha +3
Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumption, and negative impacts on…
Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network
Zhaiyu Chen, Yuanyuan Wang, Yilei Shi +1
Reliable building height estimation is essential for various urban applications. Spaceborne SAR tomography (TomoSAR) provides weather-independent, side-looking observations that ca…
Unified Primitive Proxies for Structured Shape Completion
Zhaiyu Chen, Yuqing Wang, Xiao Xiang Zhu
Structured shape completion recovers missing geometry as primitives rather than as unstructured points, which enables primitive-based surface reconstruction. Instead of following t…
Learning Generalizable Shape Completion with SIM(3) Equivariance
Yuqing Wang, Zhaiyu Chen, Xiao Xiang Zhu
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 rat…
TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset
Olaf Wysocki, Benedikt Schwab, Manoj Kumar Biswanath +31
Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple…
Parametric Point Cloud Completion for Polygonal Surface Reconstruction
Zhaiyu Chen, Yuqing Wang, Liangliang Nan +1
Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion…
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong +3
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Net…
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
Zhaiyu Chen, Yilei Shi, Liangliang Nan +2
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposi…
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
Zhaiyu Chen, Hugo Ledoux, Seyran Khademi +1
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 prob…