NAVER LABS has made available five new indoor datasets for large scale visual localization in crowded public spaces.
Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka, Martin Humenberger |
2021 |
NAVER LABS has made available five new indoor datasets for large scale visual localization in crowded public spaces.
The datasets, which have over 130,000 images, cover several floors of a large department store with shops and restaurants, a busy metro station, underground train platforms and an underground shopping mall providing an immensely rich combination of static scenes and moving objects. The datasets are first of a kind because the images are captured in large scale public spaces and in realistic conditions. They are being made available to help researchers and developers increase the applicability of visual localization algorithms in real-world applications such as AR, autonomous driving and robotics. The datasets can be directly downloaded here or by using the kapture dataset downloader.
Additional information about the datasets can be found here and on naverlabs.com/datasets.
Visual localization algorithms estimate the camera pose of a given image which is an important task in robotics, autonomous driving and in augmented reality (AR) and the results of modern visual localization methods have been promising enough for some real-world applications to have been developed such as AR navigation.
Challenges. Indoor environments differ from outdoor scenes in a number of important aspects concerning visual localization. For example, although lighting conditions in indoor spaces tend to be more constant than those in the outside world, indoor spaces are typically narrower so small camera movements can cause large changes in viewpoint. Dynamic objects like people, trolleys, TV screens, areas with no texture and repetitive patterns are also challenging for visual localization and there are many more of these in indoor spaces.
Existing datasets. In outdoor environments, the structure-from-motion (SfM) technique is effective in generating ground truth camera poses, which are necessary for all localization algorithms. Unfortunately, due to the aforementioned challenges, large-scale SfM is more difficult and often impossible to apply to indoor spaces. As a consequence, the area covered by existing indoor datasets (Stanford, ScanNet, Matterport 3D, 7 scenes, InLoc and Baidu) is only sparsely sampled by the provided images (e.g. only one image every 2 meters).
Furthermore, while the presence of people is an important aspect for a realistic dataset, privacy issues and limitations of data acquisition (standard SfM often fails) typically hinder the inclusion of crowds in state-of-the-art datasets.
NAVER LABS datasets. To contribute to the need for more training data, and in particular data of indoor environments, NAVER LABS has made available five new localization datasets acquired in challenging real-world indoor environments. The datasets were captured in a large shopping mall and a busy metro station in Seoul, South Korea and include images and depth maps obtained from a dedicated mapping platform with ten cameras and two 3D laser scanners. Compared to existing indoor datasets, the proposed datasets provide dense image sampling and cover many challenges of indoor visual localization as shown in Figure 1. In addition, the datasets consist of captures of sequences with time intervals of up to 128 days providing changes over time. To address the problem that SfM cannot be used alone to estimate precise ground truth camera poses for large indoor spaces, we developed a novel, automated pipeline that utilizes both LiDAR SLAM and SfM algorithms. The proposed pipeline applies LiDAR-based pose-graph SLAM to estimate the trajectories of our dedicated mapping platform M1X. For full details on the datasets see our CVPR 2021 paper (4) and/or article Indoor Localization Dataset V1.0 (Dept. & Metro St.)
Kapture. The datasets are provided in the unified data format kapture. Kapture is being increasingly adopted as the toolbox that accompanies it makes the execution of modern visual localization methods easy and the conversion into other formats such as OpenMVG and COLMAP simple.
Evaluation. The datasets are part of the Long-Term Visual Localization Benchmark. Localization results of your own algorithm on the test sets can be evaluated and compared against other methods. For each dataset (Gangnam Station and Hyundai Department Store), the results have to be submitted as one text file containing all floors (2 for Gangnam Station, 3 for Hyundai Department Store).
Text file format:
# image_path qw qx qy qz tx ty tz 2019-08-21_11-47-29/galaxy/AC01324968_1566355938331000.jpg -0.238452 -0.247522 -0.618811 0.706361 670.685 -16.5772 -51.8993 2019-08-21_11-47-29/galaxy/AC01324968_1566356238363000.jpg 0.701688 0.528133 -0.285814 0.383432 -634.116 -26.8537 -180.42 2019-08-21_11-47-29/galaxy/AC01324968_1566356280347000.jpg 0.464522 0.351865 -0.514493 0.629052 -239.154 -97.3573 -580.324 ...
Note that there is a script in kapture (to be used with the full_file_name option) that exports the kapture trajectory to this format.
(1) Dataset: https://www.naverlabs.com/en/datasets
(2) kapture data format: https://github.com/naver/kapture
(3) kapture toolbox (visual localization): https://github.com/naver/kapture-localization
(4) Large-scale localization datasets in crowded indoor spaces, Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka, and Martin Humenberger, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [arXiV 2105.08941]
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
——————-
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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