Currently, there have been many kinds of voxel-based 3D single stage
detectors, while point-based single stage methods are still underexplored. In
this paper, we first present a lightweight and effective point-based 3D single
stage object detector, named 3DSSD, achieving a good balance between accuracy
and efficiency. In this paradigm, all upsampling layers and refinement stage,
which are indispensable in all existing point-based methods, are abandoned to
reduce the large computation cost. We novelly propose a fusion sampling
strategy in downsampling process to make detection on less representative
points feasible. A delicate box prediction network including a candidate
generation layer, an anchor-free regression head with a 3D center-ness
assignment strategy is designed to meet with our demand of accuracy and speed.
Our paradigm is an elegant single stage anchor-free framework, showing great
superiority to other existing methods. We evaluate 3DSSD on widely used KITTI
dataset and more challenging nuScenes dataset. Our method outperforms all
state-of-the-art voxel-based single stage methods by a large margin, and has
comparable performance to two stage point-based methods as well, with inference
speed more than 25 FPS, 2x faster than former state-of-the-art point-based
methods.

本文提出一种名为 3DSSD 的轻量级且高效的基于点云的三维单级物体检测器，采用了融合采样策略以及候选生成层、无锚点回归头、三维中心性分配策略的结构设计，其精度和速度都表现出很好的平衡，并且在 KITTI 和 nuScenes 数据集上取得了比同类方法显著更好的性能。

3DSSD：基于点云的三维单级物体检测器

3DSSD: Point-based 3D Single Stage Object Detector

We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
https://github.com/RuochenFan/S4Net.

本文介绍了一种单级特征显著实例分割框架，利用分类无关属性设计出一种新型分割网络分支，并对其设计进行详尽的分析，并在公开基准测试中展示了其超越其他选择的结果。