Junru Lin

selected publication

1. SIURO

   Rapid Testing in COVID and Modified SIR Model

   Chen, J.*, Li, R.* and Lin, J.*

   SIAM Undergraduate Research Online, 2022

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   The COIVD pandemic has swept the globe since 2019, posing a grave threat to human life. There are multiple ways for the government to control the pandemic, including promoting the vaccination, limiting the number of people in public places, requiring people to wear masks in public places, and suggesting infected people isolate themselves. In this paper, we used a compartmental model to analyze the spread of COVID-19 under the promotion of rapid tests. The result shows that popularization of rapid tests may have a significant impact on controlling the pandemic. With an estimated minimum requirement for the use of rapid tests, we are able to put forward suggestions on reasonable ways to curtail the pandemic.

   @article{covid,
     title = {Rapid Testing in COVID and Modified SIR Model},
     author = {{Chen, J.*, Li, R.* and Lin, J.*}},
     journal = {SIAM Undergraduate Research Online,},
     volume = {15},
     year = {2022},
     doi = {10.1137/21S1460399},
     publisher = {Society for Industrial and Applied Mathematics,},
   }

2. IROS

   Ternary-type Opacity and Hybrid Odometry for RGB-only NeRF-SLAM

   Junru Lin, Asen Nachkov, Songyou Peng, and 2 more authors

   2023

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   In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information, relying solely on RGB inputs. The key to unlocking the full potential of NeRF in such a challenging context lies in the integration of real-world priors. A crucial prior we explore is the binary opacity prior of 3D space with opaque objects. To effectively incorporate this prior into the NeRF framework, we introduce a ternary-type opacity (TT) model instead, which categorizes points on a ray intersecting a surface into three regions: before, on, and behind the surface. This enables a more accurate rendering of depth, subsequently improving the performance of image warping techniques. Therefore, we further propose a novel hybrid odometry (HO) scheme that merges bundle adjustment and warping-based localization. Our integrated approach of TT and HO achieves state-of-the-art performance on synthetic and real-world datasets, in terms of both speed and accuracy. This breakthrough underscores the potential of NeRF-SLAM in navigating complex environments with high fidelity.

   @misc{lin2023ternarytypeopacityhybridodometry,
     title = {Ternary-type Opacity and Hybrid Odometry for RGB-only NeRF-SLAM},
     author = {Lin, Junru and Nachkov, Asen and Peng, Songyou and Gool, Luc Van and Paudel, Danda Pani},
     year = {2023},
     eprint = {2312.13332},
     archiveprefix = {arXiv},
     primaryclass = {cs.CV},
   }