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

TANet++: Triple Attention Network with Filtered Pointcloud on 3D Detection