# PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

arXiv: https://arxiv.org/abs/2003.02392 · HTML: https://mlsanity.com/paper/2003.02392/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 25 of 25)

- [NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation](https://mlsanity.com/paper/2103.12292/) (2021)
- [HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion](https://mlsanity.com/paper/2304.00932/) (2023)
- [RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments](https://mlsanity.com/paper/2211.11238/) (2022)
- [LightLoc: Learning Outdoor LiDAR Localization at Light Speed](https://mlsanity.com/paper/2503.17814/) (2025)
- [RadarLoc: Learning to Relocalize in FMCW Radar](https://mlsanity.com/paper/2103.11562/) (2021)
- [PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking](https://mlsanity.com/paper/2504.20359/) (2025)
- [TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation](https://mlsanity.com/paper/2304.07728/) (2023)
- [FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training](https://mlsanity.com/paper/2410.00702/) (2024)
- [Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps](https://mlsanity.com/paper/2301.08957/) (2023)
- [FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization](https://mlsanity.com/paper/2303.06872/) (2023)
- [BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model](https://mlsanity.com/paper/2503.11372/) (2025)
- [UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input](https://mlsanity.com/paper/2307.00741/) (2023)
- [Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization](https://mlsanity.com/paper/2504.07677/) (2025)
- [V2VLoc: Robust GNSS-Free Collaborative Perception via LiDAR Localization](https://mlsanity.com/paper/2511.14247/) (2025)
- [LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization](https://mlsanity.com/paper/2604.11355/) (2026)
- [Diffusion Based Robust LiDAR Place Recognition](https://mlsanity.com/paper/2504.12412/) (2025)
- [ThermalLoc: A Vision Transformer-Based Approach for Robust Thermal Camera Relocalization in Large-Scale Environments](https://mlsanity.com/paper/2506.18268/) (2025)
- [From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization](https://mlsanity.com/paper/2602.03198/) (2026)
- [SOAR: Regression-based LiDAR Relocalization for UAVs](https://mlsanity.com/paper/2602.13267/) (2026)
- [MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes](https://mlsanity.com/paper/2606.08744/) (2026)
- [Learning 1-Bit LiDAR-based Localization with Auxiliary Objective](https://mlsanity.com/paper/2606.27729/) (2026)
- [UQ-Loc: Uncertainty-Aware LiDAR Scene Coordinate Regression](https://mlsanity.com/paper/2608.06307/) (2026)
- [LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization](https://mlsanity.com/paper/2608.15317/) (2026)
- [ReLoc: Rethinking Scene Coordinate Regression Architecture for Robust Outdoor LiDAR-based Localization](https://mlsanity.com/paper/2609.33344/) (2026)
- [Temporal-Aware Fusion for Robust Outdoor LiDAR Localization](https://mlsanity.com/paper/2609.36432/) (2026)

## Abstract (arXiv)

In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360deg LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance.