  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.
