  Most existing point cloud based 3D object detectors focus on the tasks of
classification and box regression. However, another bottleneck in this area is
achieving an accurate detection confidence for the Non-Maximum Suppression
(NMS) post-processing. In this paper, we add a 3D IoU prediction branch to the
regular classification and regression branches. The predicted IoU is used as
the detection confidence for NMS. In order to obtain a more accurate IoU
prediction, we propose a 3D IoU-Net with IoU sensitive feature learning and an
IoU alignment operation. To obtain a perspective-invariant prediction head, we
propose an Attentive Corner Aggregation (ACA) module by aggregating a local
point cloud feature from each perspective of eight corners and adaptively
weighting the contribution of each perspective with different attentions. We
propose a Corner Geometry Encoding (CGE) module for geometry information
embedding. To the best of our knowledge, this is the first time geometric
embedding information has been introduced in proposal feature learning. These
two feature parts are then adaptively fused by a multi-layer perceptron (MLP)
network as our IoU sensitive feature. The IoU alignment operation is introduced
to resolve the mismatching between the bounding box regression head and IoU
prediction, thereby further enhancing the accuracy of IoU prediction. The
experimental results on the KITTI car detection benchmark show that 3D IoU-Net
with IoU perception achieves state-of-the-art performance.
