# STA-VPR: Spatio-temporal Alignment for Visual Place Recognition

arXiv: https://arxiv.org/abs/2103.13580 · HTML: https://mlsanity.com/paper/2103.13580/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes](https://mlsanity.com/paper/1811.03032/) (2018)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Experiments with Random Projection](https://mlsanity.com/paper/1301.3849/) (2013)
- [Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition](https://mlsanity.com/paper/1801.05078/) (2018)
- [Localizing Discriminative Visual Landmarks for Place Recognition](https://mlsanity.com/paper/1904.06635/) (2019)
- [A Hybrid Compact Neural Architecture for Visual Place Recognition](https://mlsanity.com/paper/1910.06840/) (2019)
- [Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization](https://mlsanity.com/paper/1706.07144/) (2017)

## Built on by (most cited first, 15 of 15)

- [TransVPR: Transformer-based place recognition with multi-level attention aggregation](https://mlsanity.com/paper/2201.02001/) (2022)
- [CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2402.19231/) (2024)
- [Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition](https://mlsanity.com/paper/2402.14505/) (2024)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://mlsanity.com/paper/2502.16601/) (2025)
- [StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2212.00937/) (2022)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [EDTformer: An Efficient Decoder Transformer for Visual Place Recognition](https://mlsanity.com/paper/2412.00784/) (2024)
- [AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition](https://mlsanity.com/paper/2310.05184/) (2023)
- [MambaPlace:Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms](https://mlsanity.com/paper/2408.15740/) (2024)
- [Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition](https://mlsanity.com/paper/2202.06470/) (2022)
- [Deep Homography Estimation for Visual Place Recognition](https://mlsanity.com/paper/2402.16086/) (2024)
- [Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2202.13427/) (2022)
- [StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2503.06601/) (2025)
- [Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition](https://mlsanity.com/paper/2201.03212/) (2022)

## Abstract (arXiv)

Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the middle layers of CNNs are more robust to drastic appearance changes than handcrafted features and high-layer features. Unfortunately, the holistic mid-layer features lack robustness to large viewpoint changes. Here we split the holistic mid-layer features into local features, and propose an adaptive dynamic time warping (DTW) algorithm to align local features from the spatial domain while measuring the distance between two images. This realizes viewpoint-invariant and condition-invariant place recognition. Meanwhile, a local matching DTW (LM-DTW) algorithm is applied to perform image sequence matching based on temporal alignment, which achieves further improvements and ensures linear time complexity. We perform extensive experiments on five representative VPR datasets. The results show that the proposed method significantly improves the CNN-based methods. Moreover, our method outperforms several state-of-the-art methods while maintaining good run-time performance. This work provides a novel way to boost the performance of CNN methods without any re-training for VPR. The code is available at https://github.com/Lu-Feng/STA-VPR.