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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 -
Camera Pose Estimation using Implicit Distortion Models
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout ...Conference Paper -
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. ...Conference Paper -
MBA-VO: Motion Blur Aware Visual Odometry
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within exposure time. This allows us to actively compensate for any motion ...Conference Paper -
Orthographic-Perspective Epipolar Geometry
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many applications. They can e.g. model 2D maps ...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 -
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image once and for all, which can yield poorly-localized features and propagate large errors to the final geometry. In this paper, we refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views: we first adjust ...Conference Paper -
Revisiting Radial Distortion Absolute Pose
(2020)2019 IEEE/CVF International Conference on Computer Vision (ICCV)Conference Paper -
Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We ...Conference Paper