# BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR

arXiv: https://arxiv.org/abs/2206.15154 · HTML: https://mlsanity.com/paper/2206.15154/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation](https://mlsanity.com/paper/2008.01550/) (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)
- [LiDAR Iris for Loop-Closure Detection](https://mlsanity.com/paper/1912.03825/) (2019)
- [Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling](https://mlsanity.com/paper/2011.14497/) (2020)
- [Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection](https://mlsanity.com/paper/2003.05656/) (2020)
- [SSC: Semantic Scan Context for Large-Scale Place Recognition](https://mlsanity.com/paper/2107.00382/) (2021)
- [SemSegMap- 3D Segment-Based Semantic Localization](https://mlsanity.com/paper/2107.14715/) (2021)
- [PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding](https://mlsanity.com/paper/2108.00552/) (2021)
- [LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic](https://mlsanity.com/paper/2003.04708/) (2020)

## Built on by (most cited first, 10 of 10)

- [G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model](https://mlsanity.com/paper/2308.11573/) (2023)
- [Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation](https://mlsanity.com/paper/2302.06149/) (2023)
- [MSSPlace: Multi-Sensor Place Recognition with Visual and Text Semantics](https://mlsanity.com/paper/2407.15663/) (2024)
- [SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention](https://mlsanity.com/paper/2308.03718/) (2023)
- [That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation](https://mlsanity.com/paper/2403.04755/) (2024)
- [REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation](https://mlsanity.com/paper/2503.03599/) (2025)
- [Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data](https://mlsanity.com/paper/2402.17653/) (2024)
- [QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives](https://mlsanity.com/paper/2412.02998/) (2024)
- [Introspection in Learned Semantic Scene Graph Localisation](https://mlsanity.com/paper/2510.07053/) (2025)
- [Scene Representation using 360deg Saliency Graph and its Application in Vision-based Indoor Navigation](https://mlsanity.com/paper/2603.20353/) (2026)

## Abstract (arXiv)

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4% recall at 100% precision where the next closest competitor follows with 64.9%. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10 cm and 0.33 deg.