Add link to paper

.gitattributes

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+ycbv_models.zip filter=lfs diff=lfs merge=lfs -text
+test_pbr.zip filter=lfs diff=lfs merge=lfs -text
+val_pbr.zip filter=lfs diff=lfs merge=lfs -text
+train_pbr.zip filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
 license: cc-by-sa-4.0
-pretty_name: YCB-Ev SD
 size_categories:
 - 10K<n<100K
 task_categories:
 - object-detection
 - robotics
+pretty_name: YCB-Ev SD
 tags:
 - 3d
 - image
-- event-camera
-- 6dof-pose-estimation
-- bop
-- ycb-video
-- synthetic
 ---
-# YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation
-
-Synthetic training data has become indispensable for frame-based 6D object pose estimation, yet an equivalent large-scale resource for event cameras is still missing in the YCB/BOP setting. We close this gap with YCB-Ev~SD, a dataset of 50,000 synthetic event sequences at standard-definition (SD) resolution. The sequences are generated from Physically Based Rendering (PBR) scenes of YCB-Video objects following the BOP challenge methodology, using simulated linear camera motion so that both foreground objects and background structure produce events. We adopt SD resolution (640x480px) because HD event sensors have been shown to be outperformed by SD sensors under fast motion due to increased temporal noise, and because SD matches the resolution of the existing BOP PBR images.
 
-Building on this resource, we present the first systematic ablation of event-to-image representations for CNN-based 6D pose estimation. Time-surfaces with linear decay and dual-channel polarity encoding perform best, with polarity encoding contributing the largest individual gain: +8.2 and +6.4 percentage points for histograms and time-surfaces, respectively. Finally, we validate sim-to-real transfer on real event-camera captures acquired with an improved calibration pipeline (8.3ms synchronization accuracy).
-
-https://arxiv.org/abs/2511.11344
-
-## Dataset Structure
+# YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation
 
-- train_pbr / val_pbr / test_pbr - synthetic PBR event sequences (40K / 5K / 5K views)
-- test - real event-camera captures (5 scenes, ~11,700 frames)
+Paper: [YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation](https://huggingface.co/papers/2511.11344)
 
+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.
+ 
+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. 
+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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ycbv_models.zip

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