Yue Pan

Ph.D. Student

Contact:
Email: yue.pan@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 29 05
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.003

Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: Google Scholar | Github | Linkedln

Short CV

Yue Pan is a PhD student at the University of Bonn since June 2022. He received his Master degree in Geomatics in 2022 from ETH Zurich. He received his Bachelor degree in Geomatics Engineering in 2019. He has been a visiting PhD student at ETH Zurich in spring 2024.

Research Interests

SLAM
3D Reconstruction
Navigation

Projects

PhenoRob – Robotics and Phenotyping for Sustainable Crop Production (DFG Cluster of Excellence)

Awards

Geosuisse/IGS prize 2022 for excellent master thesis and academic record in Geomatics

Publications

2025

Y. Pan, X. Zhong, L. Jin, L. Wiesmann, M. Popović, J. Behley, and C. Stachniss, “PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map,” Arxiv preprint, vol. arXiv:2502.05752, 2025.
[BibTeX] [PDF]

Robots require high-fidelity reconstructions of their environment for effective operation. Such scene representations should be both, geometrically accurate and photorealistic to support downstream tasks. While this can be achieved by building distance fields from range sensors and radiance fields from cameras, the scalable incremental mapping of both fields consistently and at the same time with high quality remains challenging. In this paper, we propose a novel map representation that unifies a continuous signed distance field and a Gaussian splatting radiance field within an elastic and compact point-based implicit neural map. By enforcing geometric consistency between these fields, we achieve mutual improvements by exploiting both modalities. We devise a LiDAR-visual SLAM system called PINGS using the proposed map representation and evaluate it on several challenging large-scale datasets. Experimental results demonstrate that PINGS can incrementally build globally consistent distance and radiance fields encoded with a compact set of neural points. Compared to the state-of-the-art methods, PINGS achieves superior photometric and geometric rendering at novel views by leveraging the constraints from the distance field. Furthermore, by utilizing dense photometric cues and multi-view consistency from the radiance field, PINGS produces more accurate distance fields, leading to improved odometry estimation and mesh reconstruction.

@article{pan2025arxiv,
author = {Y. Pan and X. Zhong and L. Jin and L. Wiesmann and M. Popovi\'c and J. Behley and C. Stachniss},
title = {{PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map}},
journal = arxiv,
year = 2025,
volume = {arXiv:2502.05752},
url = {https://arxiv.org/pdf/2502.05752},
abstract = {Robots require high-fidelity reconstructions of their environment for effective operation. Such scene representations should be both, geometrically accurate and photorealistic to support downstream tasks. While this can be achieved by building distance fields from range sensors and radiance fields from cameras, the scalable incremental mapping of both fields consistently and at the same time with high quality remains challenging. In this paper, we propose a novel map representation that unifies a continuous signed distance field and a Gaussian splatting radiance field within an elastic and compact point-based implicit neural map. By enforcing geometric consistency between these fields, we achieve mutual improvements by exploiting both modalities. We devise a LiDAR-visual SLAM system called PINGS using the proposed map representation and evaluate it on several challenging large-scale datasets. Experimental results demonstrate that PINGS can incrementally build globally consistent distance and radiance fields encoded with a compact set of neural points. Compared to the state-of-the-art methods, PINGS achieves superior photometric and geometric rendering at novel views by leveraging the constraints from the distance field. Furthermore, by utilizing dense photometric cues and multi-view consistency from the radiance field, PINGS produces more accurate distance fields, leading to improved odometry estimation and mesh reconstruction.}
}

H. Kuang, Y. Pan, X. Zhong, L. Wiesmann, J. Behley, and C. Stachniss, “Improving Indoor Localization Accuracy by Using an Efficient Implicit Neural Map Representation,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2025.
[BibTeX]

@inproceedings{huang2025icra,
author = {H. Kuang and Y. Pan and X. Zhong and L. Wiesmann and J. Behley and Stachniss, C.},
title = {{Improving Indoor Localization Accuracy by Using an Efficient Implicit Neural Map Representation}},
booktitle = icra,
year = {2025},
note = {Accepted},
}

F. Magistri, T. Läbe, E. Marks, S. Nagulavancha, Y. Pan, C. Smitt, L. Klingbeil, M. Halstead, H. Kuhlmann, C. McCool, J. Behley, and C. Stachniss, “A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2025.
[BibTeX] [PDF]

@inproceedings{magistri2025icra,
author = {F. Magistri and T. L\"abe and E. Marks and S. Nagulavancha and Y. Pan and C. Smitt and L. Klingbeil and M. Halstead and H. Kuhlmann and C. McCool and J. Behley and C. Stachniss},
title = {{A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics}},
booktitle = icra,
year = 2025,
url = {https://arxiv.org/pdf/2407.13304v1},
note = {Accepted},
}

2024

L. Jin, X. Zhong, Y. Pan, J. Behley, C. Stachniss, and M. Popovic, “ActiveGS: Active Scene Reconstruction using Gaussian Splatting,” Arxiv preprint, vol. arXiv:2412.17769, 2024.
[BibTeX] [PDF]

@article{jin2024arxiv,
author = {L. Jin and X. Zhong and Y. Pan and J. Behley and C. Stachniss and M. Popovic},
title = {{ActiveGS: Active Scene Reconstruction using Gaussian Splatting}},
journal = arxiv,
year = 2024,
volume = {arXiv:2412.17769},
url = {https://arxiv.org/pdf/2412.17769},
}

Y. Pan, X. Zhong, L. Wiesmann, T. Posewsky, J. Behley, and C. Stachniss, “PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency,” IEEE Trans. on Robotics (TRO), vol. 40, p. 4045–4064, 2024. doi:10.1109/TRO.2024.3422055
[BibTeX] [PDF] [Code]

@article{pan2024tro,
author = {Y. Pan and X. Zhong and L. Wiesmann and T. Posewsky and J. Behley and C. Stachniss},
title = {{PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency}},
journal = tro,
year = {2024},
pages = {4045--4064},
volume = {40},
doi = {10.1109/TRO.2024.3422055},
codeurl = {https://github.com/PRBonn/PIN_SLAM},
}

F. Magistri, Y. Pan, J. Bartels, J. Behley, C. Stachniss, and C. Lehnert, “Improving Robotic Fruit Harvesting Within Cluttered Environments Through 3D Shape Completion,” Ieee robotics and automation letters (ra-l), vol. 9, iss. 8, p. 7357–7364, 2024. doi:10.1109/LRA.2024.3421788
[BibTeX] [PDF]

@article{magistri2024ral,
author = {F. Magistri and Y. Pan and J. Bartels and J. Behley and C. Stachniss and C. Lehnert},
title = {{Improving Robotic Fruit Harvesting Within Cluttered Environments
Through 3D Shape Completion}},
journal = ral,
volume = {9},
number = {8},
pages = {7357--7364},
year = 2024,
doi = {10.1109/LRA.2024.3421788},
}

L. Jin, H. Kuang, Y. Pan, C. Stachniss, and M. Popović, “STAIR: Semantic-Targeted Active Implicit Reconstruction,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2024. doi:10.1109/IROS58592.2024.10801401
[BibTeX] [PDF] [Code]

@inproceedings{jin2024iros,
author = {L. Jin and H. Kuang and Y. Pan and C. Stachniss and M. Popovi\'c},
title = {{STAIR: Semantic-Targeted Active Implicit Reconstruction}},
booktitle = iros,
year = 2024,
codeurl = {https://github.com/dmar-bonn/stair},
doi = {10.1109/IROS58592.2024.10801401},
}

X. Zhong, Y. Pan, C. Stachniss, and J. Behley, “3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. doi:10.1109/CVPR52733.2024.01460
[BibTeX] [PDF] [Code] [Video]

@inproceedings{zhong2024cvpr,
author = {X. Zhong and Y. Pan and C. Stachniss and J. Behley},
title = {{3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation}},
booktitle = cvpr,
year = 2024,
doi = {10.1109/CVPR52733.2024.01460},
codeurl = {https://github.com/PRBonn/4dNDF},
videourl ={https://youtu.be/pRNKRcTkxjs}
}

2023

Y. Pan, F. Magistri, T. Läbe, E. Marks, C. Smitt, C. S. McCool, J. Behley, and C. Stachniss, “Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2023.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{pan2023iros,
author = {Y. Pan and F. Magistri and T. L\"abe and E. Marks and C. Smitt and C.S. McCool and J. Behley and C. Stachniss},
title = {{Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots}},
booktitle = iros,
year = 2023,
codeurl = {https://github.com/PRBonn/HortiMapping},
videourl = {https://youtu.be/fSyHBhskjqA}
}

L. Wiesmann, T. Guadagnino, I. Vizzo, N. Zimmerman, Y. Pan, H. Kuang, J. Behley, and C. Stachniss, “LocNDF: Neural Distance Field Mapping for Robot Localization,” Ieee robotics and automation letters (ra-l), vol. 8, iss. 8, p. 4999–5006, 2023. doi:10.1109/LRA.2023.3291274
[BibTeX] [PDF] [Code] [Video]

@article{wiesmann2023ral-icra,
author = {L. Wiesmann and T. Guadagnino and I. Vizzo and N. Zimmerman and Y. Pan and H. Kuang and J. Behley and C. Stachniss},
title = {{LocNDF: Neural Distance Field Mapping for Robot Localization}},
journal = ral,
volume = {8},
number = {8},
pages = {4999--5006},
year = 2023,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2023ral-icra.pdf},
issn = {2377-3766},
doi = {10.1109/LRA.2023.3291274},
codeurl = {https://github.com/PRBonn/LocNDF},
videourl = {https://youtu.be/-0idH21BpMI},
}

X. Zhong, Y. Pan, J. Behley, and C. Stachniss, “SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{zhong2023icra,
author = {Zhong, Xingguang and Pan, Yue and Behley, Jens and Stachniss, Cyrill},
title = {{SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations}},
booktitle = icra,
year = 2023,
codeurl = {https://github.com/PRBonn/SHINE_mapping},
videourl = {https://youtu.be/jRqIupJgQZE},
}

2022

Y. Pan, Y. Kompis, L. Bartolomei, R. Mascaro, C. Stachniss, and M. Chli, “Voxfield: Non-Projective Signed Distance Fields for Online Planning and 3D Reconstruction,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2022.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{pan2022iros,
title = {{Voxfield: Non-Projective Signed Distance Fields for Online Planning and 3D Reconstruction}},
author = {Y. Pan and Y. Kompis and L. Bartolomei and R. Mascaro and C. Stachniss and M. Chli},
booktitle = iros,
year = {2022},
codeurl = {https://github.com/VIS4ROB-lab/voxfield},
videourl ={https://youtu.be/JS_yeq-GR4A},
}