Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions

arXiv:2411.14865 · eess.IV, cs.CV, cs.RO · Submitted 2024-11-22 · Read on arXiv

eess.IV, cs.CV, cs.RO

Submitted: 2024-11-22

Updated: 2026-10-10

Comments: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT). Code and benchmark resources are publicly available at https://github.com/ZhonghuaYi/optical_flow_robustness_benchmark

Code: https://github.com/ZhonghuaYi/optical_flow_robustness_benchmark

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 13× in this paper · explained in 5 episodes
• Optical Flow — 10× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Corruption Robustness — 4× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes

Sources

• Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models
• Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems
• PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions
• Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
• Robustness of SAM: Segment Anything Under Corruptions and Beyond
• Denoising Diffusion Implicit Models
• PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines

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