  In this paper, we focus on the problem of feature learning in the presence of
scale imbalance for 6-DoF grasp detection and propose a novel approach to
especially address the difficulty in dealing with small-scale samples. A
Multi-scale Cylinder Grouping (MsCG) module is presented to enhance local
geometry representation by combining multi-scale cylinder features and global
context. Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced
Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the
samples whose scales are in low frequency by apriori weights while OBS captures
more points on small-scale objects with the help of an auxiliary segmentation
network. They alleviate the influence of the uneven distribution of grasp
scales in training and inference respectively. In addition, Noisy-clean Mix
(NcM) data augmentation is introduced to facilitate training, aiming to bridge
the domain gap between synthetic and raw scenes in an efficient way by
generating more data which mix them into single ones at instance-level.
Extensive experiments are conducted on the GraspNet-1Billion benchmark and
competitive results are reached with significant gains on small-scale cases.
Besides, the performance of real-world grasping highlights its generalization
ability. Our code is available at
https://github.com/mahaoxiang822/Scale-Balanced-Grasp.
