# TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields

arXiv: https://arxiv.org/abs/2105.11605 · HTML: https://mlsanity.com/paper/2105.11605/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 71 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 30 of 49)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)

## Built on by (most cited first, 29 of 29)

- [Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training](https://mlsanity.com/paper/2203.00972/) (2022)
- [BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images](https://mlsanity.com/paper/2302.14325/) (2023)
- [CASSPR: Cross Attention Single Scan Place Recognition](https://mlsanity.com/paper/2211.12542/) (2022)
- [I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections](https://mlsanity.com/paper/2303.01043/) (2023)
- [MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity](https://mlsanity.com/paper/2112.06539/) (2021)
- [CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition](https://mlsanity.com/paper/2303.17778/) (2023)
- [CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework](https://mlsanity.com/paper/2503.02593/) (2025)
- [HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views](https://mlsanity.com/paper/2503.08140/) (2025)
- [Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments](https://mlsanity.com/paper/2403.13395/) (2024)
- [SALSA: Swift Adaptive Lightweight Self-Attention for Enhanced LiDAR Place Recognition](https://mlsanity.com/paper/2407.08260/) (2024)
- [MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions](https://mlsanity.com/paper/2403.07593/) (2024)
- [ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images](https://mlsanity.com/paper/2503.04475/) (2025)
- [HOTFLoc++: End-to-End Hierarchical LiDAR Place Recognition, Re-Ranking, and 6-DoF Metric Localisation in Forests](https://mlsanity.com/paper/2511.09170/) (2025)
- [Uncertainty-Aware Lidar Place Recognition in Novel Environments](https://mlsanity.com/paper/2210.01361/) (2022)
- [SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition](https://mlsanity.com/paper/2306.01205/) (2023)
- [Vehicle-Scene Interaction: A Text-Driven 3D Lidar Place Recognition Method for Autonomous Driving](https://mlsanity.com/paper/2503.18035/) (2025)
- [VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition](https://mlsanity.com/paper/2308.12870/) (2023)
- [Ranking-aware Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2505.07198/) (2025)
- [MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity](https://mlsanity.com/paper/2505.17591/) (2025)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [SpatiaLoc: Leveraging Multi-Level Spatial Enhanced Descriptors for Cross-Modal Localization](https://mlsanity.com/paper/2601.03579/) (2026)
- [TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching](https://mlsanity.com/paper/2602.01501/) (2026)
- [Riemannian and Symplectic Geometry for Hierarchical Text-Driven Place Recognition](https://mlsanity.com/paper/2604.01598/) (2026)
- [A Deeper Look into Second-Order Feature Aggregation for LiDAR Place Recognition](https://mlsanity.com/paper/2409.15919/) (2024)
- [Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2511.15597/) (2025)
- [Loop Closure via Maximal Cliques in 3D LiDAR-Based SLAM](https://mlsanity.com/paper/2603.05397/) (2026)
- [Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition](https://mlsanity.com/paper/2603.14974/) (2026)
- [4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation](https://mlsanity.com/paper/2603.26206/) (2026)
- [DTIF: Robust Loop Closure Detection via Delaunay Triangle Topology in Complex Forests](https://mlsanity.com/paper/2607.21138/) (2026)

## Abstract (arXiv)

Place recognition plays an essential role in the field of autonomous driving and robot navigation. Point cloud based methods mainly focus on extracting global descriptors from local features of point clouds. Despite having achieved promising results, existing solutions neglect the following aspects, which may cause performance degradation: (1) huge size difference between objects in outdoor scenes; (2) moving objects that are unrelated to place recognition; (3) long-range contextual information. We illustrate that the above aspects bring challenges to extracting discriminative global descriptors. To mitigate these problems, we propose a novel method named TransLoc3D, utilizing adaptive receptive fields with a point-wise reweighting scheme to handle objects of different sizes while suppressing noises, and an external transformer to capture long-range feature dependencies. As opposed to existing architectures which adopt fixed and limited receptive fields, our method benefits from size-adaptive receptive fields as well as global contextual information, and outperforms current state-of-the-arts with significant improvements on popular datasets.