A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment

Xiaoqian Huang, Sanket Kachole, Abdulla Ayyad, Fariborz Baghaei Naeini, Dimitrios Makris, Yahya Zweiri

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations

    Abstract

    Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.

    Original languageBritish English
    Article number127
    JournalScientific Data
    Volume11
    Issue number1
    DOIs
    StatePublished - Dec 2024

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