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--- |
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license: cc-by-sa-4.0 |
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pretty_name: YCB-Ev SD |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- object-detection |
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- robotics |
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tags: |
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- 3d |
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- image |
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--- |
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# YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation |
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We introduce YCB-Ev SD, a synthetic dataset of event-camera data at standard definition (SD) resolution for 6DoF object pose estimation. While synthetic data has become fundamental in frame-based computer vision, event-based vision lacks comparable comprehensive resources. Addressing this gap, we present 50,000 event sequences of 34 ms duration each, synthesized from Physically Based Rendering (PBR) scenes of YCB-Video objects following the Benchmark for 6D Object Pose (BOP) methodology. Our generation framework employs simulated linear camera motion to ensure complete scene coverage, including background activity. |
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Through systematic evaluation of event representations for CNN-based inference, we demonstrate that time-surfaces with linear decay and dual-channel polarity encoding achieve superior pose estimation performance, outperforming exponential decay and single-channel alternatives by significant margins. Our analysis reveals that polarity information contributes most substantially to performance gains, while linear temporal encoding preserves critical motion information more effectively than exponential decay. |
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The dataset is provided in a structured format with both raw event streams and precomputed optimal representations to facilitate immediate research use and reproducible benchmarking. |
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https://arxiv.org/abs/2511.11344 |
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