🤖 AI Summary
The event-based vision community has long lacked a standardized synthetic dataset for 6DoF object pose estimation, hindering the development of deep learning methods. To address this, we introduce the first synthetic event dataset aligned with the BOP benchmark: high-fidelity event streams are generated via physically based rendering (PBR) and linear motion modeling. We propose a novel dual-channel polarity-encoded time surface representation incorporating linear temporal decay. The dataset comprises 50,000 event sequences, each 34 ms long, spanning multiple objects, viewpoints, and challenging illumination conditions. Experiments demonstrate that our representation significantly outperforms single-channel and exponentially decaying alternatives, yielding a 12.7% improvement in pose estimation accuracy. This work establishes the first standardized synthetic resource for event-based 6DoF pose estimation, providing essential infrastructure for algorithm development and fair, reproducible evaluation.
📝 Abstract
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. The dataset is publicly available at https://huggingface.co/datasets/paroj/ycbev_sd.