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Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the ...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 -
Infrastructure-Based Multi-camera Calibration Using Radial Projections
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Multi-camera systems are an important sensor platform for intelligent systems such as self-driving cars. Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. However, extrinsic calibration of systems with little to no visual overlap between the cameras is a challenge. Given the camera intrinsics, infrastructure-based calibration techniques are able to estimate the extrinsics using 3D ...Conference Paper -
Handcrafted Outlier Detection Revisited
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, ...Conference Paper