Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks.
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced.
The adaptive adjusting term is composed of two complementary factors: 1) quantity factor, which pays more attention to tail classes, and 2) difficulty factor, which adaptively pays more attention to hard instances in the training process.
Some deep neural networks are invariant to some input transformations, such as Pointnet is permutation invariant to the input point cloud.
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent.
Ranked #2 on 3D Point Cloud Classification on ModelNet40-C
We have conducted extensive autonomous landing experiments in a variety of familiar or completely unknown environments, verifying that our model can adaptively balance the accuracy and speed, and the UAV can robustly select a safe landing site.