init with README

README.md

@@ -0,0 +1,30 @@
+---
+license: cc-by-sa-4.0
+pretty_name: YCB-Ev SD
+size_categories:
+- 10K<n<100K
+task_categories:
+- object-detection
+- robotics
+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
+
+- train_pbr / val_pbr / test_pbr - synthetic PBR event sequences (40K / 5K / 5K views)
+- test - real event-camera captures (5 scenes, ~11,700 frames)
+