MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR
Not on the map. Why?
Abstract (arXiv)
Recent years have seen significant improvement in absolute camera pose estimation, paving the way for pervasive markerless Augmented Reality (AR). However, accurate absolute pose estimation techniques are computation- and storage-heavy, requiring computation offloading. As such, AR systems rely on visual-inertial odometry (VIO) to track the device's relative pose between requests to the server. However, VIO suffers from drift, requiring frequent absolute repositioning. This paper introduces MobileARLoc, a new framework for on-device large-scale markerless mobile AR that combines an absolute pose regressor (APR) with a local VIO tracking system. Absolute pose regressors (APRs) provide fast on-device pose estimation at the cost of reduced accuracy. To address APR accuracy and reduce VIO drift, MobileARLoc creates a feedback loop where VIO pose estimations refine the APR predictions. The VIO system identifies reliable predictions of APR, which are then used to compensate for the VIO drift. We comprehensively evaluate MobileARLoc through dataset simulations. MobileARLoc halves the error compared to the underlying APR and achieve fast (80,ms) on-device inference speed.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 21 Jan 2024.
Builds on
23 of 40 references.
References · 23
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 556
- 2019D2-Net: A Trainable CNN for Joint Detection and Description of Local Featurescited by 324
- 2016Large-Scale Image Retrieval with Attentive Deep Local Featurescited by 238
- 2017Geometric Loss Functions for Camera Pose Regression with Deep Learningcited by 224
- 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
- 2017Geometry-Aware Learning of Maps for Camera Localizationcited by 161
- 2015Modelling Uncertainty in Deep Learning for Camera Relocalizationcited by 133
11 more
- 2021Learning Multi-Scene Absolute Pose Regression with Transformerscited by 108
- 2022DFNet: Enhance Absolute Pose Regression with Direct Feature Matchingcited by 82
- 2017Image-based Localization using Hourglass Networkscited by 63
- 2021Direct-PoseNet: Absolute Pose Regression with Photometric Consistencycited by 61
- 2020Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimationcited by 43
- 2021CoordiNet: uncertainty-aware pose regressor for reliable vehicle localizationcited by 34
- 20206D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inferencecited by 23
- 2021Reassessing the Limitations of CNN Methods for Camera Pose Regressioncited by 19
- 2023Long-term Visual Localization with Mobile Sensorscited by 13
- 2023A Probabilistic Framework for Visual Localization in Ambiguous Scenescited by 12
- 2022ImPosing: Implicit Pose Encoding for Efficient Visual Localizationcited by 3
Built on by
Citing papers per year: 2024: 1.
1 paper.