  In this paper, we propose an end-to-end grasp evaluation model to address the
challenging problem of localizing robot grasp configurations directly from the
point cloud. Compared to recent grasp evaluation metrics that are based on
handcrafted depth features and a convolutional neural network (CNN), our
proposed PointNetGPD is lightweight and can directly process the 3D point cloud
that locates within the gripper for grasp evaluation. Taking the raw point
cloud as input, our proposed grasp evaluation network can capture the complex
geometric structure of the contact area between the gripper and the object even
if the point cloud is very sparse. To further improve our proposed model, we
generate a larger-scale grasp dataset with 350k real point cloud and grasps
with the YCB object set for training. The performance of the proposed model is
quantitatively measured both in simulation and on robotic hardware. Experiments
on object grasping and clutter removal show that our proposed model generalizes
well to novel objects and outperforms state-of-the-art methods. Code and video
are available at
\href{https://lianghongzhuo.github.io/PointNetGPD}{https://lianghongzhuo.github.io/PointNetGPD}
