18 citations · 49 across the 12 of their papers we have counts for
12 papers
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation
Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn
Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weat…
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
Lukas Mehl, Jenny Schmalfuss, Azin Jahedi +2
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of ex…
High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)
Azin Jahedi, Maximilian Luz, Lukas Mehl +2
In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022. It is based on the MS-RAFT method, which successfully integrates several…
Attacking Motion Estimation with Adversarial Snow
Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn
Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, we exploit a r…
Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation
Azin Jahedi, Lukas Mehl, Marc Rivinius +1
Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approach…
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
Lukas Mehl, Azin Jahedi, Jenny Schmalfuss +1
Recently, neural network for scene flow estimation show impressive results on automotive data such as the KITTI benchmark. However, despite of using sophisticated rigidity assumpti…
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs
Jenny Schmalfuss, Erik Scheurer, Heng Zhao +3
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of i…
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Jenny Schmalfuss, Philipp Scholze, Andrés Bruhn
Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perfo…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…
Technical Report on Visual Quality Assessment for Frame Interpolation
Hui Men, Hanhe Lin, Vlad Hosu +3
Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpola…
ProFlow: Learning to Predict Optical Flow
Daniel Maurer, Andrés Bruhn
Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-tri…
Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation
Yong Chul Ju, Daniel Maurer, Michael Breuß +1
Most of today's state-of-the-art methods for perspective shape from shading are modelled in terms of partial differential equations (PDEs) of Hamilton-Jacobi type. To improve the r…