18 citations · 28 across the 6 of their papers we have counts for
6 papers
MS-RAFT-3D: A Multi-Scale Architecture for Recurrent Image-Based Scene Flow
Jakob Schmid, Azin Jahedi, Noah Berenguel Senn +1
Although multi-scale concepts have recently proven useful for recurrent network architectures in the field of optical flow and stereo, they have not been considered for image-based…
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning
Azin Jahedi, Maximilian Luz, Marc Rivinius +1
Attention-based motion aggregation concepts have recently shown their usefulness in optical flow estimation, in particular when it comes to handling occluded regions. However, due…
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…
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…