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.

提出了一种在 3D 物体检测中通过 IoU 预测来解决 NMS 中准确性问题的方法，使用了 3D IoU-Net 进行特征学习和准确性预测，并通过 Corner Geometry Encoding 和 Attentive Corner Aggregation 模块提取几何信息并实现对预测角度的不变性，实验结果表明，该方法在 KITTI car detection benchmark 上达到了最先进水平。