Robust Image Retrieval-based Visual Localization using Kapture

• URL: http://arxiv.org/abs/2007.13867v3
• Date: Fri, 7 Jan 2022 10:05:49 GMT
• Title: Robust Image Retrieval-based Visual Localization using Kapture
• Authors: Martin Humenberger and Yohann Cabon and Nicolas Guerin and Julien Morat and Vincent Leroy and J\'er\^ome Revaud and Philippe Rerole and No\'e Pion and Cesar de Souza and Gabriela Csurka
• Abstract summary: We present a versatile pipeline for visual localization that facilitates the use of different local and global features. We evaluate our methods on eight public datasets where they rank top on all and first on many of them. To foster future research, we release code, models, and all datasets used in this paper in the kapture format open source under a permissive BSD license.
• Score: 10.249293519246478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual localization tackles the challenge of estimating the camera pose from images by using correspondence analysis between query images and a map. This task is computation and data intensive which poses challenges on thorough evaluation of methods on various datasets. However, in order to further advance in the field, we claim that robust visual localization algorithms should be evaluated on multiple datasets covering a broad domain variety. To facilitate this, we introduce kapture, a new, flexible, unified data format and toolbox for visual localization and structure-from-motion (SFM). It enables easy usage of different datasets as well as efficient and reusable data processing. To demonstrate this, we present a versatile pipeline for visual localization that facilitates the use of different local and global features, 3D data (e.g. depth maps), non-vision sensor data (e.g. IMU, GPS, WiFi), and various processing algorithms. Using multiple configurations of the pipeline, we show the great versatility of kapture in our experiments. Furthermore, we evaluate our methods on eight public datasets where they rank top on all and first on many of them. To foster future research, we release code, models, and all datasets used in this paper in the kapture format open source under a permissive BSD license. github.com/naver/kapture, github.com/naver/kapture-localization

Related papers

• 360 in the Wild: Dataset for Depth Prediction and View Synthesis [66.58513725342125]
  We introduce a large scale 360$circ$ videos dataset in the wild. This dataset has been carefully scraped from the Internet and has been captured from various locations worldwide. Each of the 25K images constituting our dataset is provided with its respective camera's pose and depth map.
  arXiv  Detail & Related papers  (2024-06-27T05:26:38Z)
• MeshVPR: Citywide Visual Place Recognition Using 3D Meshes [18.168206222895282]
  Mesh-based scene representation offers a promising direction for simplifying large-scale hierarchical visual localization pipelines. While existing work demonstrates the viability of meshes for visual localization, the impact of using synthetic databases rendered from them in visual place recognition remains largely unexplored. We propose MeshVPR, a novel VPR pipeline that utilizes a lightweight features alignment framework to bridge the gap between real-world and synthetic domains.
  arXiv  Detail & Related papers  (2024-06-04T20:45:53Z)
• DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping [3.5047603107971397]
  We tightly integrate the trainable deep dense bundle adjustment (DBA) with multi-sensor information through a factor graph. A pipeline for visual-inertial integration is firstly developed, which provides the minimum ability of metric-scale localization and mapping. The results validate the superior localization performance of our approach, which enables real-time dense mapping in large-scale environments.
  arXiv  Detail & Related papers  (2024-03-20T16:20:54Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images [58.720142291102135]
  We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment. The dataset is based on the popular Habitat simulator, in which it is possible to generate indoor scenes using both own sensor data and open datasets.
  arXiv  Detail & Related papers  (2022-12-30T12:20:56Z)
• Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics [58.720142291102135]
  We present a fully automated pipeline to generate a synthetic dataset for instance segmentation in four steps. We first scrape images for the objects of interest from popular image search engines. We compare three different methods for image selection: Object-agnostic pre-processing, manual image selection and CNN-based image selection.
  arXiv  Detail & Related papers  (2022-10-18T12:49:04Z)
• Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments [32.34672033386747]
  We present a novel, accurate, and fast pole extraction approach based on geometric features that runs online. Our method performs all computations directly on range images generated from 3D LiDAR scans. We use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.
  arXiv  Detail & Related papers  (2022-08-15T17:58:08Z)
• CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization [44.97567243883994]
  We propose a new benchmark for visual localization in outdoor scenes using crowd-sourced data. We show that our dataset is very challenging, with all evaluated methods failing on its hardest parts. As part of the dataset release, we provide the tooling used to generate it, enabling efficient and effective 2D correspondence annotation.
  arXiv  Detail & Related papers  (2021-09-09T19:25:48Z)
• Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization [21.375640354558044]
  State estimation approaches based on local sensors are drifting-prone for long-range missions as error accumulates. We introduce the cross-scale dataset and a methodology to produce additional data from cross-modality sources. We propose a framework that learns cross-scale visual representations without supervision.
  arXiv  Detail & Related papers  (2021-09-09T08:08:54Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)
• Single Image Depth Estimation Trained via Depth from Defocus Cues [105.67073923825842]
  Estimating depth from a single RGB image is a fundamental task in computer vision. In this work, we rely, instead of different views, on depth from focus cues. We present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
  arXiv  Detail & Related papers  (2020-01-14T20:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.