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

TL;DR

TANet++ enhances 3D object detection accuracy by introducing a novel training strategy that filters noisy point cloud data, leading to significant performance improvements on benchmarks.

Contribution

The paper proposes TANet++, a 3D detection model that employs a new training strategy to mitigate noise effects in point clouds, improving detection robustness and accuracy.

Findings

AP score on JRDB increased by 8.98 points compared to TANet.

Filtering training data improves model performance with noisy point clouds.

TANet++ outperforms state-of-the-art methods on KITTI and JRDB benchmarks.

Abstract

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.