FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
Eventpillars: Pillar-based efficient representations for event data.Proceedings of the AAAI Conference on Artificial Intelligence, 39(3):2861–2869, 2025
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A survey that categorizes event camera representation learning into dense-based and sparse-based methods, examining design choices, benchmarks, and open problems.
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FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
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A Systematic Survey on Event Camera Representation Learning
A survey that categorizes event camera representation learning into dense-based and sparse-based methods, examining design choices, benchmarks, and open problems.