NYC-Indoor-VPR: A Comprehensive Indoor Visual Place Recognition Dataset with Diverse Scenes and Long-Term Appearance Changes

Core Concepts

This paper introduces the NYC-Indoor-VPR dataset, a unique and rich collection of over 36,000 images from 13 distinct crowded indoor scenes in New York City, captured over a year-long period under varying lighting conditions and appearance changes. The authors also propose a semi-automatic annotation approach to efficiently and accurately generate ground truth topometric locations for the dataset, enabling the evaluation of state-of-the-art visual place recognition algorithms.

Abstract

The paper introduces the NYC-Indoor-VPR dataset, a large-scale indoor visual place recognition (VPR) dataset that addresses several key challenges in this domain:

Diverse indoor scenes: The dataset covers 13 distinct crowded indoor scenes in New York City, including buildings such as the Oculus, NYU Silver Center, Bobst Library, Morton Williams Supermarket, and Metropolitan Museum of Art. This diversity represents a broad range of indoor environments.

Long-term appearance changes: The dataset was collected over a one-year period, capturing significant changes in illumination, dynamic objects, and furniture distribution across the scenes. This allows for evaluating the robustness of VPR algorithms to long-term appearance variations.

Anonymized pedestrians: The dataset employs semantic segmentation to anonymize pedestrians in the images, maintaining privacy while allowing VPR algorithms to focus on invariant environmental features.

To establish ground truth for the dataset, the authors propose a semi-automatic annotation method that efficiently and accurately matches trajectories and generates image pairs with their relative topometric locations. This method overcomes the limitations of existing approaches, such as Structure from Motion and SLAM, which struggle to accurately reconstruct large indoor scenes.
The authors benchmark several state-of-the-art VPR algorithms on the NYC-Indoor-VPR dataset, revealing its challenges. The results show that the dataset poses significant difficulties for current VPR methods, with the Fulton subway station and Oculus scenes being particularly challenging due to perceptual aliasing and view obstruction by dynamic objects. This highlights the value of the NYC-Indoor-VPR dataset for advancing indoor VPR research.

Stats

The NYC-Indoor-VPR dataset contains over 36,000 images from 13 distinct indoor scenes in New York City, captured over a one-year period.
The dataset covers the following scenes:

The Oculus (floor 2): 13,933 images
NYU Silver Center (floors 2-6, 9): 6,450 images
Bobst Library (floors -1, 4, 5): 10,929 images
Morton Williams Supermarket (floor 1): 2,237 images
Metropolitan Museum of Art (floor 1): 1,266 images
Fulton Subway Station (floor 1): 4,627 images

Quotes

"NYC-Indoor-VPR images were captured in buildings such as The Oculus and the Bobst Library, which typically have a large flow of pedestrians. We anonymized these pedestrians in the images to reduce their exposure to personally identifiable information."
"NYC-Indoor-VPR spans a year and includes images captured in buildings that undergo significant visual changes over time. For instance, goods in the supermarket vary and storefronts in the shopping mall are subject to change. This variability in the dataset allows us to test the performance of the VPR algorithms with fewer invariant features in the images."

Key Insights Distilled From

NYC-Indoor-VPR

by Diwei Sheng,... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00504.pdf

Deeper Inquiries

How can the semi-automatic annotation method be further improved to handle more complex indoor environments, such as multi-floor buildings or environments with significant occlusions

The semi-automatic annotation method can be enhanced to tackle more complex indoor environments by incorporating advanced techniques. One approach could involve integrating depth sensors or LiDAR technology to capture 3D information, enabling the annotation of multi-floor buildings accurately. By combining visual data with depth information, the system can create a more comprehensive understanding of the environment's spatial layout. Additionally, implementing machine learning algorithms for automated trajectory matching in challenging scenarios, such as significant occlusions, could improve the accuracy and efficiency of the annotation process. These algorithms could learn to identify and handle occlusions by analyzing patterns in the trajectories and images, leading to more robust annotations in complex indoor settings.

What additional techniques or architectural modifications could be explored to improve the performance of state-of-the-art VPR algorithms on the NYC-Indoor-VPR dataset, particularly in challenging scenes like the Fulton subway station and Oculus

To enhance the performance of state-of-the-art VPR algorithms on the NYC-Indoor-VPR dataset, especially in challenging scenes like the Fulton subway station and Oculus, several techniques can be explored. One potential approach is to investigate the use of attention mechanisms in the network architectures to focus on relevant features and suppress noise from dynamic objects or perceptual aliasing. Architectural modifications like incorporating recurrent neural networks (RNNs) or graph neural networks (GNNs) could help capture long-term dependencies in the trajectories and improve matching accuracy in scenes with repetitive structures. Furthermore, data augmentation techniques tailored to simulate challenging scenarios, such as varying lighting conditions and dynamic object occlusions, could enhance the algorithms' robustness and generalization capabilities.

How could the NYC-Indoor-VPR dataset be leveraged to develop novel VPR algorithms that are specifically designed to handle the unique challenges of indoor environments, such as perceptual aliasing and dynamic object occlusions

The NYC-Indoor-VPR dataset presents a unique opportunity to develop novel VPR algorithms specifically designed for indoor environments' challenges. One approach could involve leveraging the dataset to train deep learning models with attention mechanisms that can adaptively focus on relevant spatial features while ignoring distractions like dynamic objects. Additionally, exploring unsupervised or self-supervised learning techniques to learn invariant representations from the dataset could help mitigate issues like perceptual aliasing and appearance changes. Furthermore, developing hybrid models that combine traditional feature extraction methods with deep learning approaches could offer a comprehensive solution for handling the diverse challenges present in indoor VPR scenarios. By iteratively training and testing these novel algorithms on the NYC-Indoor-VPR dataset, researchers can refine and optimize their performance to achieve state-of-the-art results in indoor visual place recognition.