Most of the existing single-stage and two-stage 3D object detectors are
anchor-based methods, while the efficient but challenging anchor-free
single-stage 3D object detection is not well investigated. Recent studies on 2D
object detection show that the anchor-free methods also are of great potential.
However, the unordered and sparse properties of point clouds prevent us from
directly leveraging the advanced 2D methods on 3D point clouds. We overcome
this by converting the voxel-based sparse 3D feature volumes into the sparse 2D
feature maps. We propose an attentive module to fit the sparse feature maps to
dense mostly on the object regions through the deformable convolution tower and
the supervised mask-guided attention. By directly regressing the 3D bounding
box from the enhanced and dense feature maps, we construct a novel single-stage
3D detector for point clouds in an anchor-free manner. We propose an IoU-based
detection confidence re-calibration scheme to improve the correlation between
the detection confidence score and the accuracy of the bounding box regression.
Our code is publicly available at https://github.com/jialeli1/MGAF-3DSSD.

提出了一种基于稀疏特征图的关注模块的单级无 Anchor 3D 对象检测方法，通过将基于体素的稀疏 3D 特征数据转换为稀疏 2D 特征图，直接从增强和稠密的 2D 特征映射中回归出 3D 边界框，并进一步提出了一种基于 IoU 的检测置信度重新校准方案，最终可用于点云数据的目标检测。