# L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration

arXiv: https://arxiv.org/abs/2406.03298 · HTML: https://mlsanity.com/paper/2406.03298/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 59 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer](https://mlsanity.com/paper/2308.03768/) (2023)
- [Learning multiview 3D point cloud registration](https://mlsanity.com/paper/2001.05119/) (2020)
- [You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors](https://mlsanity.com/paper/2109.00182/) (2021)
- [Learning Compact Geometric Features](https://mlsanity.com/paper/1709.05056/) (2017)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV](https://mlsanity.com/paper/1909.06700/) (2019)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [Reconstructing Objects in-the-wild for Realistic Sensor Simulation](https://mlsanity.com/paper/2311.05602/) (2023)
- [PCGen: Point Cloud Generator for LiDAR Simulation](https://mlsanity.com/paper/2210.08738/) (2022)
- [Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild](https://mlsanity.com/paper/2101.06860/) (2021)
- [Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting](https://mlsanity.com/paper/2304.00467/) (2023)
- [MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving](https://mlsanity.com/paper/2309.16715/) (2023)
- [Improvements to Target-Based 3D LiDAR to Camera Calibration](https://mlsanity.com/paper/1910.03126/) (2019)
- [TagSLAM: Robust SLAM with Fiducial Markers](https://mlsanity.com/paper/1910.00679/) (2019)
- [Mapping and Localization from Planar Markers](https://mlsanity.com/paper/1606.00151/) (2016)
- [Navigation of a Self-Driving Vehicle Using One Fiducial Marker](https://mlsanity.com/paper/2104.12954/) (2021)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [Application of Ghost-DeblurGAN to Fiducial Marker Detection](https://mlsanity.com/paper/2109.03379/) (2021)
- [Intensity Image-based LiDAR Fiducial Marker System](https://mlsanity.com/paper/2203.01816/) (2022)
- [Autonomous Vision-based UAV Landing with Collision Avoidance using Deep Learning](https://mlsanity.com/paper/2109.08628/) (2021)
- [Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation](https://mlsanity.com/paper/2309.16110/) (2023)

## Built on by (most cited first, 2 of 2)

- [HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation](https://mlsanity.com/paper/2502.10606/) (2025)
- [SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy](https://mlsanity.com/paper/2605.12247/) (2026)

## Abstract (arXiv)

Point cloud registration is a prerequisite for many applications in computer vision and robotics. Most existing methods focus on pairwise registration of two point clouds with high overlap. Although there have been some methods for low overlap cases, they struggle in degraded scenarios. This paper introduces a novel framework dubbed L-PR, designed to register unordered low overlap multiview point clouds leveraging LiDAR fiducial markers. We refer to them as LiDAR fiducial markers, but they are the same as the popular AprilTag and ArUco markers, thin sheets of paper that do not affect the 3D geometry of the environment. We first propose an improved adaptive threshold marker detection method to provide robust detection results when the viewpoints among point clouds change dramatically. Then, we formulate the unordered multiview point cloud registration problem as a maximum a-posteriori (MAP) problem and develop a framework consisting of two levels of graphs to address it. The first-level graph, constructed as a weighted graph, is designed to efficiently and optimally infer initial values of scan poses from the unordered set. The second-level graph is constructed as a factor graph. By globally optimizing the variables on the graph, including scan poses, marker poses, and marker corner positions, we tackle the MAP problem. We conduct both qualitative and quantitative experiments to demonstrate that the proposed method surpasses previous state-of-the-art (SOTA) methods and to showcase that L-PR can serve as a low-cost and efficient tool for 3D asset collection and training data collection. In particular, we collect a new dataset named Livox-3DMatch using L-PR and incorporate it into the training of the SOTA learning-based method, SGHR, which brings evident improvements for SGHR on various benchmarks.