# MambaPlace:Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms

arXiv: https://arxiv.org/abs/2408.15740 · HTML: https://mlsanity.com/paper/2408.15740/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 20 of 20)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [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)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [Structured State Space Models for In-Context Reinforcement Learning](https://mlsanity.com/paper/2303.03982/) (2023)
- [Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy](https://mlsanity.com/paper/2403.06467/) (2024)
- [Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection](https://mlsanity.com/paper/2406.10700/) (2024)
- [Text2Pos: Text-to-Point-Cloud Cross-Modal Localization](https://mlsanity.com/paper/2203.15125/) (2022)
- [Text2Loc: 3D Point Cloud Localization from Natural Language](https://mlsanity.com/paper/2311.15977/) (2023)
- [RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation](https://mlsanity.com/paper/2404.02457/) (2024)
- [Facing Off World Model Backbones: RNNs, Transformers, and S4](https://mlsanity.com/paper/2307.02064/) (2023)
- [PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis](https://mlsanity.com/paper/2405.15463/) (2024)
- [CASSPR: Cross Attention Single Scan Place Recognition](https://mlsanity.com/paper/2211.12542/) (2022)
- [Text to Point Cloud Localization with Relation-Enhanced Transformer](https://mlsanity.com/paper/2301.05372/) (2023)
- [STA-VPR: Spatio-temporal Alignment for Visual Place Recognition](https://mlsanity.com/paper/2103.13580/) (2021)
- [EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition](https://mlsanity.com/paper/2405.18065/) (2024)
- [DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking](https://mlsanity.com/paper/2207.08044/) (2022)
- [Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing](https://mlsanity.com/paper/2212.05752/) (2022)

## Built on by (most cited first, 7 of 7)

- [GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap](https://mlsanity.com/paper/2501.08575/) (2025)
- [SpatiaLoc: Leveraging Multi-Level Spatial Enhanced Descriptors for Cross-Modal Localization](https://mlsanity.com/paper/2601.03579/) (2026)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [Riemannian and Symplectic Geometry for Hierarchical Text-Driven Place Recognition](https://mlsanity.com/paper/2604.01598/) (2026)
- [Text2Loc++: Generalizing 3D Point Cloud Localization from Natural Language](https://mlsanity.com/paper/2511.15308/) (2025)
- [Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2511.15597/) (2025)
- [PosEviLoc: Position-Conditioned Spatial Evidence for Language-Based 3D Localization](https://mlsanity.com/paper/2609.23534/) (2026)

## Abstract (arXiv)

Vision Language Place Recognition (VLVPR) enhances robot localization performance by incorporating natural language descriptions from images. By utilizing language information, VLVPR directs robot place matching, overcoming the constraint of solely depending on vision. The essence of multimodal fusion lies in mining the complementary information between different modalities. However, general fusion methods rely on traditional neural architectures and are not well equipped to capture the dynamics of cross modal interactions, especially in the presence of complex intra modal and inter modal correlations. To this end, this paper proposes a novel coarse to fine and end to end connected cross modal place recognition framework, called MambaPlace. In the coarse localization stage, the text description and 3D point cloud are encoded by the pretrained T5 and instance encoder, respectively. They are then processed using Text Attention Mamba (TAM) and Point Clouds Mamba (PCM) for data enhancement and alignment. In the subsequent fine localization stage, the features of the text description and 3D point cloud are cross modally fused and further enhanced through cascaded Cross Attention Mamba (CCAM). Finally, we predict the positional offset from the fused text point cloud features, achieving the most accurate localization. Extensive experiments show that MambaPlace achieves improved localization accuracy on the KITTI360Pose dataset compared to the state of the art methods.