Search Results for author: David Schubert

Found 8 papers, 4 papers with code

Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets

no code implementations24 Nov 2022 David Schubert, Pritha Gupta, Marcel Wever

In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data.

Anomaly Detection Meta-Learning

Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction

1 code implementation CVPR 2022 Christiane Sommer, Lu Sang, David Schubert, Daniel Cremers

We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations.

3D Reconstruction

Square Root Marginalization for Sliding-Window Bundle Adjustment

no code implementations ICCV 2021 Nikolaus Demmel, David Schubert, Christiane Sommer, Daniel Cremers, Vladyslav Usenko

The square root formulation pervades three major aspects of our optimization-based sliding-window estimator: for bundle adjustment we eliminate landmark variables with nullspace projection; to store the marginalization prior we employ a matrix square root of the Hessian; and when marginalizing old poses we avoid forming normal equations and update the square root prior directly with a specialized QR decomposition.

Efficient Derivative Computation for Cumulative B-Splines on Lie Groups

6 code implementations CVPR 2020 Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers

Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required.

Rolling-Shutter Modelling for Direct Visual-Inertial Odometry

no code implementations4 Nov 2019 David Schubert, Nikolaus Demmel, Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers

The visual part of the system performs a photometric bundle adjustment on a sparse set of points.

Visual-Inertial Mapping with Non-Linear Factor Recovery

7 code implementations13 Apr 2019 Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers

We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.

Motion Estimation

Direct Sparse Odometry with Rolling Shutter

no code implementations ECCV 2018 David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.

Visual Odometry

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

4 code implementations17 Apr 2018 David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers

For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.

Visual Odometry

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