# That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation

arXiv: https://arxiv.org/abs/2403.04755 · HTML: https://mlsanity.com/paper/2403.04755/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation](https://mlsanity.com/paper/2008.01550/) (2020)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [DeepGMR: Learning Latent Gaussian Mixture Models for Registration](https://mlsanity.com/paper/2008.09088/) (2020)
- [GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer](https://mlsanity.com/paper/2308.03768/) (2023)
- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression](https://mlsanity.com/paper/2005.07178/) (2020)
- [SegMatch: Segment based loop-closure for 3D point clouds](https://mlsanity.com/paper/1609.07720/) (2016)
- [Semantic Graph Based Place Recognition for 3D Point Clouds](https://mlsanity.com/paper/2008.11459/) (2020)
- [SegMap: Segment-based mapping and localization using data-driven descriptors](https://mlsanity.com/paper/1909.12837/) (2019)
- [SSC: Semantic Scan Context for Large-Scale Place Recognition](https://mlsanity.com/paper/2107.00382/) (2021)
- [Data-Efficient Decentralized Visual SLAM](https://mlsanity.com/paper/1710.05772/) (2017)
- [PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention](https://mlsanity.com/paper/2209.09699/) (2022)
- [SemSegMap- 3D Segment-Based Semantic Localization](https://mlsanity.com/paper/2107.14715/) (2021)
- [BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR](https://mlsanity.com/paper/2206.15154/) (2022)
- [InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning](https://mlsanity.com/paper/2305.09552/) (2023)
- [SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds](https://mlsanity.com/paper/2206.13117/) (2022)
- [SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention](https://mlsanity.com/paper/2308.03718/) (2023)
- [Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models](https://mlsanity.com/paper/2308.04220/) (2023)
- [CAPRICORN: Communication Aware Place Recognition using Interpretable Constellations of Objects in Robot Networks](https://mlsanity.com/paper/1910.08810/) (2019)

## Built on by (most cited first, 3 of 3)

- [NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms](https://mlsanity.com/paper/2410.11031/) (2024)
- [Towards Global Localization using Multi-Modal Object-Instance Re-Identification](https://mlsanity.com/paper/2409.12002/) (2024)
- [Introspection in Learned Semantic Scene Graph Localisation](https://mlsanity.com/paper/2510.07053/) (2025)

## Abstract (arXiv)

This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.