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。

基于掩码引导注意力的无锚点三维单阶段点云检测器

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for  Point Cloud

Temporal action localization is an important yet challenging task in video
understanding. Typically, such a task aims at inferring both the action
category and localization of the start and end frame for each action instance
in a long, untrimmed video.While most current models achieve good results by
using pre-defined anchors and numerous actionness, such methods could be
bothered with both large number of outputs and heavy tuning of locations and
sizes corresponding to different anchors. Instead, anchor-free methods is
lighter, getting rid of redundant hyper-parameters, but gains few attention. In
this paper, we propose the first purely anchor-free temporal localization
method, which is both efficient and effective. Our model includes (i) an
end-to-end trainable basic predictor, (ii) a saliency-based refinement module
to gather more valuable boundary features for each proposal with a novel
boundary pooling, and (iii) several consistency constraints to make sure our
model can find the accurate boundary given arbitrary proposals. Extensive
experiments show that our method beats all anchor-based and actionness-guided
methods with a remarkable margin on THUMOS14, achieving state-of-the-art
results, and comparable ones on ActivityNet v1.3. Code is available at
this https URL

本文提出了第一个完全基于无锚点的时间定位方法，该模型通过使用一种基本的可训练推荐器、一个基于显着性的细化模块以及若干一致性约束来实现。实验证明，我们的方法在 THUMOS14 上击败了所有锚点和动作导向方法，取得了最先进的结果，在 ActivityNet v1.3 上取得了可比较的结果。