# CCL: Continual Contrastive Learning for LiDAR Place Recognition

arXiv: https://arxiv.org/abs/2303.13952 · HTML: https://mlsanity.com/paper/2303.13952/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 24 Mar 2023.

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

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [MinkLoc3D: Point Cloud Based Large-Scale Place Recognition](https://mlsanity.com/paper/2011.04530/) (2020)
- [OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition](https://mlsanity.com/paper/2203.03397/) (2022)
- [LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition](https://mlsanity.com/paper/2109.08336/) (2021)
- [Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling](https://mlsanity.com/paper/2011.14497/) (2020)
- [DiSCO: Differentiable Scan Context with Orientation](https://mlsanity.com/paper/2010.10949/) (2020)
- [InCloud: Incremental Learning for Point Cloud Place Recognition](https://mlsanity.com/paper/2203.00807/) (2022)
- [SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data](https://mlsanity.com/paper/2209.07951/) (2022)
- [Generalizing to the Open World: Deep Visual Odometry with Online Adaptation](https://mlsanity.com/paper/2103.15279/) (2021)
- [CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data](https://mlsanity.com/paper/2302.01665/) (2023)
- [AirLoop: Lifelong Loop Closure Detection](https://mlsanity.com/paper/2109.08975/) (2021)
- [Contrastive Learning for Unsupervised Radar Place Recognition](https://mlsanity.com/paper/2110.02744/) (2021)
- [DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition](https://mlsanity.com/paper/2111.13838/) (2021)

## Built on by (most cited first, 8 of 8)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Explicit Interaction for Fusion-Based Place Recognition](https://mlsanity.com/paper/2402.17264/) (2024)
- [LifelongPR: Lifelong point cloud place recognition based on sample replay and prompt learning](https://mlsanity.com/paper/2507.10034/) (2025)
- [TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation](https://mlsanity.com/paper/2404.01587/) (2024)
- [Ranking-aware Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2505.07198/) (2025)
- [OptiCorNet: Optimizing Sequence-Based Context Correlation for Visual Place Recognition](https://mlsanity.com/paper/2507.14477/) (2025)
- [Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2511.15597/) (2025)
- [RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution](https://mlsanity.com/paper/2601.07855/) (2026)

## Abstract (arXiv)

Place recognition is an essential and challenging task in loop closing and global localization for robotics and autonomous driving applications. Benefiting from the recent advances in deep learning techniques, the performance of LiDAR place recognition (LPR) has been greatly improved. However, current deep learning-based methods suffer from two major problems: poor generalization ability and catastrophic forgetting. In this paper, we propose a continual contrastive learning method, named CCL, to tackle the catastrophic forgetting problem and generally improve the robustness of LPR approaches. Our CCL constructs a contrastive feature pool and utilizes contrastive loss to train more transferable representations of places. When transferred into new environments, our CCL continuously reviews the contrastive memory bank and applies a distribution-based knowledge distillation to maintain the retrieval ability of the past data while continually learning to recognize new places from the new data. We thoroughly evaluate our approach on Oxford, MulRan, and PNV datasets using three different LPR methods. The experimental results show that our CCL consistently improves the performance of different methods in different environments outperforming the state-of-the-art continual learning method. The implementation of our method has been released at https://github.com/cloudcjf/CCL.