In this paper, we propose a Monocular 3D Single Stage object Detector
(M3DSSD) with feature alignment and asymmetric non-local attention. Current
anchor-based monocular 3D object detection methods suffer from feature
mismatching. To overcome this, we propose a two-step feature alignment
approach. In the first step, the shape alignment is performed to enable the
receptive field of the feature map to focus on the pre-defined anchors with
high confidence scores. In the second step, the center alignment is used to
align the features at 2D/3D centers. Further, it is often difficult to learn
global information and capture long-range relationships, which are important
for the depth prediction of objects. Therefore, we propose a novel asymmetric
non-local attention block with multi-scale sampling to extract depth-wise
features. The proposed M3DSSD achieves significantly better performance than
the monocular 3D object detection methods on the KITTI dataset, in both 3D
object detection and bird's eye view tasks.

本文提出了一个在特征对齐和非对称非局部注意力机制下的单目三维单级物体检测器 (M3DSSD)，借助两步特征对齐来解决现有锚点法单目三维目标检测方法在特征匹配方面的缺陷，并提出了一种新的非对称非局部注意力块，实现更好的物体深度预测效果，并在 KITTI 数据集上取得了显著的成果。