18 citations · 30 across the 7 of their papers we have counts for
7 papers
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Victor Oei, Jenny Schmalfuss, Lukas Mehl +5
Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hen…
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…