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Found 5 papers, 5 papers with code
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning
Attention-based motion aggregation concepts have recently shown their usefulness in optical flow estimation, in particular when it comes to handling occluded regions.
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
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 existing benchmarks nor their evaluation methodology.
High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)
In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022.
Ranked #3 on
Optical Flow Estimation
on Spring
Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation
Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness.
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand.
Ranked #1 on
Scene Flow Estimation
on Spring
