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Privacy Preserving Localization via Coordinate Permutations
(2023)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving methods. In this paper, we overcome this ...Conference Paper -
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant progress has been made, researchers are still mostly driven by unrealistic benchmarks not representative of real-world AR scenarios. In particular, benchmarks are often based on small-scale datasets with low scene diversity, captured from stationary cameras, and ...Conference Paper -
Privacy Preserving Partial Localization
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recently proposed privacy preserving solutions for cloud-based localization rely on lifting traditional point-based maps to randomized 3D line clouds. While the lifted representation is effective in concealing private information, there are two fundamental limitations. First, without careful construction of the line clouds, the representation is vulnerable to density-based inversion attacks. Secondly, after successful localization, the ...Conference Paper -
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
(2022)2022 International Conference on Robotics and Automation (ICRA)For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed ...Conference Paper -
Privacy Preserving Localization and Mapping from Uncalibrated Cameras
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy ...Conference Paper -
Cross-Descriptor Visual Localization and Mapping
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems. Most state-of-the-art approaches rely on local features to establish correspondences between images. In this paper, we present three novel scenarios for localization and mapping which require the continuous update of feature representations and the ability to match across different feature types. While localization and ...Conference Paper -
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by ...Conference Paper -
NeuralFusion: Online Depth Fusion in Latent Space
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our ...Conference Paper -
RoutedFusion: Learning Real-Time Depth Map Fusion
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capable machine learning-based method for depth map fusion. Similar to the seminal depth map fusion approach by Curless and Levoy, we only update a local group of voxels to ensure real-time ...Conference Paper -
Privacy Preserving Structure-from-Motion
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived ...Conference Paper