# Place Recognition under Occlusion and Changing Appearance via Disentangled Representations

arXiv: https://arxiv.org/abs/2211.11439 · HTML: https://mlsanity.com/paper/2211.11439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1809.05852/) (2018)
- [Disentangling factors of variation in deep representations using adversarial training](https://mlsanity.com/paper/1611.03383/) (2016)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [ComboGAN: Unrestrained Scalability for Image Domain Translation](https://mlsanity.com/paper/1712.06909/) (2017)
- [Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer](https://mlsanity.com/paper/1803.03341/) (2018)
- [Discovering Hidden Factors of Variation in Deep Networks](https://mlsanity.com/paper/1412.6583/) (2014)
- [Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments](https://mlsanity.com/paper/1909.10184/) (2019)
- [Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization](https://mlsanity.com/paper/2009.07719/) (2020)

## Built on by (most cited first, 1 of 1)

- [DeepCompass: AI-driven Location-Orientation Synchronization for Navigating Platforms](https://mlsanity.com/paper/2311.12805/) (2023)

## Abstract (arXiv)

Place recognition is a critical and challenging task for mobile robots, aiming to retrieve an image captured at the same place as a query image from a database. Existing methods tend to fail while robots move autonomously under occlusion (e.g., car, bus, truck) and changing appearance (e.g., illumination changes, seasonal variation). Because they encode the image into only one code, entangling place features with appearance and occlusion features. To overcome this limitation, we propose PROCA, an unsupervised approach to decompose the image representation into three codes: a place code used as a descriptor to retrieve images, an appearance code that captures appearance properties, and an occlusion code that encodes occlusion content. Extensive experiments show that our model outperforms the state-of-the-art methods. Our code and data are available at https://github.com/rover-xingyu/PROCA.