Liren Jin
Ph.D. Student Contact:Email: ljin@nulluni-bonn.de
Tel: +49 – 228 – 73 – 29 04
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.001
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn
Profiles: Google Scholar | Github | Linkedln
Research Interests
- Active Perception
- 3D Reconstruction
- Implicit Neural Representations
Short CV
Liren Jin is a Ph.D. student at the University of Bonn since March 2021. He is advised by Prof. Marija Popović and Prof. Cyrill Stachniss. He received his Master’s degree in 2020 from RWTH Aachen and Bachelor’s degree in 2016, both in Mechanical Engineering. During his Master’s, he spent 6 months at Volkswagen AG as an intern working on multi-robot navigation.
Projects
- PhenoRob – Robotics and Phenotyping for Sustainable Crop Production (DFG Cluster of Excellence)
Publications
2024
- 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.
[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} }
- S. Pan, L. Jin, X. Huang, C. Stachniss, M. Popović, and M. Bennewitz, “Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2024.
[BibTeX] [PDF]@inproceedings{pan2024iros, author = {S. Pan and L. Jin and X. Huang and C. Stachniss and M. Popovi\'c and M. Bennewitz}, title = {{Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning}}, booktitle = iros, year = 2024, }
- S. Pan, L. Jin, X. Huang, C. Stachniss, M. Popovic, and M. Bennewitz, “Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning,” in In proc. of the icra workshop on neural fields in robotics (robonerf), 2024.
[BibTeX]@inproceedings{pan2024icraws, title={{Exploiting Priors from 3D Diffusion Models for {RGB}-Based One-Shot View Planning}}, author={S. Pan and L. Jin and X. Huang and C. Stachniss and M. Popovic and M. Bennewitz}, booktitle={In Proc. of the ICRA Workshop On Neural Fields In Robotics (RoboNerF)}, year={2024}, }
2023
- L. Jin, X. Chen, J. Rückin, and M. Popović, “NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2023.
[BibTeX] [PDF] [Code]@inproceedings{jin2023iros, title = {{NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering}}, booktitle = iros, author = {Jin, Liren and Chen, Xieyuanli and Rückin, Julius and Popović, Marija}, year = {2023}, codeurl = {https://github.com/dmar-bonn/neu-nbv}, }
2022
- J. Rückin, L. Jin, F. Magistri, C. Stachniss, and M. Popović, “Informative Path Planning for Active Learning in Aerial Semantic Mapping,” in Proc. of the ieee/rsj intl. conf. on intelligent robots and systems (iros), 2022.
[BibTeX] [PDF] [Code]@InProceedings{rueckin2022iros, author = {J. R{\"u}ckin and L. Jin and F. Magistri and C. Stachniss and M. Popovi\'c}, title = {{Informative Path Planning for Active Learning in Aerial Semantic Mapping}}, booktitle = iros, year = {2022}, codeurl = {https://github.com/dmar-bonn/ipp-al}, }
- J. Rückin, L. Jin, and M. Popović, “Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2022.
[BibTeX] [PDF] [Code]@inproceedings{rueckin2022icra, author = {R{\"u}ckin, Julius and Jin, Liren and Popović, Marija}, booktitle = icra, title = {{Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing}}, year = {2022}, codeurl = {https://github.com/dmar-bonn/ipp-rl}, }
- L. Jin, J. Rückin, S. H. Kiss, T. Vidal-Calleja, and M. Popović, “Adaptive-resolution field mapping using Gaussian process fusion with integral kernels,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, p. 7471–7478, 2022.
[BibTeX] [PDF] [Code]@article{jin2022ral, title={{Adaptive-resolution field mapping using Gaussian process fusion with integral kernels}}, author={Jin, Liren and R{\"u}ckin, Julius and Kiss, Stefan H and Vidal-Calleja, Teresa and Popovi{\'c}, Marija}, journal=ral, volume={7}, number={3}, pages={7471--7478}, year={2022}, codeurl = {https://github.com/dmar-bonn/argpf_mapping}, }