Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter
<|reference_start|>Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter: In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.<|reference_end|>
@article{lim2024multi-object,
    title={Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter},
    author={Seunghyeon Lim, Youngjae Yoo, Jun Ki Lee, Byoung-Tak Zhang},
    journal={arXiv preprint arXiv:2403.12449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12449},
    primaryClass={cs.RO}
}