  Grasping algorithms have evolved from planar depth grasping to utilizing
point cloud information, allowing for application in a wider range of
scenarios. However, data-driven grasps based on models trained on basic
open-source datasets may not perform well on novel objects, which are often
required in different scenarios, necessitating fine-tuning using new objects.
The data driving these algorithms essentially corresponds to the closing region
of the hand in 6D pose, and due to the uniqueness of 6D pose, synthetic
annotation or real-machine annotation methods are typically employed. Acquiring
large amounts of data with real-machine annotation is challenging, making
synthetic annotation a common practice. However, obtaining annotated 6D pose
data using conventional methods is extremely time-consuming. Therefore, we
propose a method to quickly acquire data for novel objects, enabling more
efficient fine-tuning. Our method primarily samples grasp orientations to
generate and annotate grasps. Experimental results demonstrate that our
fine-tuning process for a new object is 400 \% faster than other methods.
Furthermore, we propose an optimized grasp annotation framework that accounts
for the effects of the gripper closing, making the annotations more reasonable.
Upon acceptance of this paper, we will release our algorithm as open-source.
