论文标题
在混乱的场景中朝量表平衡的6-DOF抓握检测
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes
论文作者
论文摘要
在本文中，我们将重点放在特征学习的问题上，存在于6-DOF GRASP检测的规模不平衡的情况下，并提出了一种新颖的方法，特别是解决了处理小规模样本的困难。提出了多尺度的圆柱体组（MSCG）模块，以通过组合多尺度缸体和全局环境来增强本地几何表示。此外，设计了量表平衡学习（SBL）损失和对象平衡采样（OBS）策略，其中SBL扩大了样品的梯度，其样品的尺度在APRIORI权重较低的情况下，而OBS借助辅助分割网络在小规模对象上捕获了更多点。它们分别减轻了掌握量表分别在训练和推理中的不均匀分布的影响。此外，引入了嘈杂清洁混合物（NCM）数据增强以促进训练，旨在通过生成更多的数据将它们混合成实例级别的单个数据，以有效的方式弥合合成场景和原始场景之间的域间隙。在Graspnet 1亿个基准上进行了广泛的实验，并在小规模案例上达到了竞争性结果。此外，现实世界抓住的表现突出了其概括能力。我们的代码可从https://github.com/mahaoxiang822/scale-balanced-grasp获得。
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.
