  TANet is one of state-of-the-art 3D object detection method on KITTI and JRDB
benchmark, the network contains a Triple Attention module and Coarse-to-Fine
Regression module to improve the robustness and accuracy of 3D Detection.
However, since the original input data (point clouds) contains a lot of noise
during collecting the data, which will further affect the training of the
model. For example, the object is far from the robot, the sensor is difficult
to obtain enough pointcloud. If the objects only contains few point clouds, and
the samples are fed into model with the normal samples together during
training, the detector will be difficult to distinguish the individual with few
pointcloud belong to object or background. In this paper, we propose TANet++ to
improve the performance on 3D Detection, which adopt a novel training strategy
on training the TANet. In order to reduce the negative impact by the weak
samples, the training strategy previously filtered the training data, and then
the TANet++ is trained by the rest of data. The experimental results shows that
AP score of TANet++ is 8.98 higher than TANet on JRDB benchmark.
