publications

2024

1. 

   Surface Reconstruction Using Rotation Systems

   Ruiqi Cui, Emil Toftegaard Gæde, Eva Rotenberg, Leif Kobbelt, and J Andreas Bærentzen

   arXiv preprint arXiv:2402.01893, 2024

   Abs Code Website

   Inspired by the seminal result that a graph and an associated rotation system uniquely determine the topology of a closed manifold, we propose a combinatorial method for reconstruction of surfaces from points. Our method constructs a spanning tree and a rotation system. Since the tree is trivially a planar graph, its rotation system determines a genus zero surface with a single face which we proceed to incrementally refine by inserting edges to split faces and thus merging them. In order to raise the genus, special handles are added by inserting edges between different faces and thus merging them. We apply our method to a wide range of input point clouds in order to investigate its effectiveness, and we compare our method to several other surface reconstruction methods. We find that our method offers better control over outlier classification, i.e. which points to include in the reconstructed surface, and also more control over the topology of the reconstructed surface.

2023

1. PA-Net: Plane Attention Network for real-time urban scene reconstruction

   Yilin Liu, Ruiqi Cui, Ke Xie, Minglun Gong, and Hui Huang

   Computers & Graphics, 2023

2021

1. 

   Aerial path planning for online real-time exploration and offline high-quality reconstruction of large-scale urban scenes

   Yilin Liu, Ruiqi Cui, Ke Xie, Minglun Gong, and Hui Huang

   ACM Transactions on Graphics (TOG), 2021

   Abs Website

   Existing approaches have shown that, through carefully planning flight trajectories, images captured by Unmanned Aerial Vehicles (UAVs) can be used to reconstruct high-quality 3D models for real environments. These approaches greatly simplify and cut the cost of large-scale urban scene reconstruction. However, to properly capture height discontinuities in urban scenes, all state-of-the-art methods require prior knowledge on scene geometry and hence, additional prepossessing steps are needed before performing the actual image acquisition flights. To address this limitation and to make urban modeling techniques even more accessible, we present a real-time explore-and-reconstruct planning algorithm that does not require any prior knowledge for the scenes. Using only captured 2D images, we estimate 3D bounding boxes for buildings on-the-fly and use them to guide online path planning for both scene exploration and building observation. Experimental results demonstrate that the aerial paths planned by our algorithm in real-time for unknown environments support reconstructing 3D models with comparable qualities and lead to shorter flight air time.

2020

1. Statistically driven model for efficient analysis of few-photon transport in waveguide quantum electrodynamics

   Ruiqi Cui, Dian Tan, and Yuecheng Shen

   JOSA B, 2020

   Abs

   Understanding transport properties in quantum nanophotonics plays a central role in designing few-photon devices, yet it suffers from a longstanding extensive computational burden. In this work, we propose a statistically driven model with a tremendously eased computational burden, which is based on the deep understanding of the few-photon spontaneous emission process. By utilizing phenomenological, statistically driven inter-photon offset parameters, the proposed model expedites the transport calculation with a three-order-of-magnitude enhancement of speed in contrast to conventional numerical approaches. We showcase the two-photon transport computation benchmarked by the rigorous analytical approach. Our work provides an efficient tool for designing few-photon nano-devices, and it significantly deepens the understanding of correlated quantum many-body physics.