The first FPGA-based hardware architecture for the Contrast Maximization algorithm in event-based vision achieves over 200x faster motion parameter estimation with high energy efficiency.
In: Eu- ropean Conference on Computer Vision
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2026 2verdicts
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A single attention-based model trained on synthetic wide-baseline event data achieves zero-shot feature matching across unseen datasets with a reported 37.7% improvement over prior event matching methods.
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FPGA-Based Hardware Architecture for Contrast Maximization in Event-Based Vision
The first FPGA-based hardware architecture for the Contrast Maximization algorithm in event-based vision achieves over 200x faster motion parameter estimation with high energy efficiency.
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Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras
A single attention-based model trained on synthetic wide-baseline event data achieves zero-shot feature matching across unseen datasets with a reported 37.7% improvement over prior event matching methods.