  3D objectness estimation, namely discovering semantic objects from 3D scene,
is a challenging and significant task in 3D understanding. In this paper, we
propose a 3D objectness method working in a bottom-up manner. Beginning with
over-segmented 3D segments, we iteratively group them into object proposals by
learning an ingenious grouping predictor to determine whether two 3D segments
can be grouped or not. To enhance robustness, a novel regret mechanism is
presented to withdraw incorrect grouping operations. Hence the irreparable
consequences brought by mistaken grouping in prior bottom-up works can be
greatly reduced. Our experiments show that our method outperforms
state-of-the-art 3D objectness methods with a small number of proposals in two
difficult datasets, GMU-kitchen and CTD. Further ablation study also
demonstrates the effectiveness of our grouping predictor and regret mechanism.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing