Non-uniformed 3D sparse data, e.g., point clouds or voxels in different
spatial positions, make contribution to the task of 3D object detection in
different ways. Existing basic components in sparse convolutional networks
(Sparse CNNs) process all sparse data, regardless of regular or submanifold
sparse convolution. In this paper, we introduce two new modules to enhance the
capability of Sparse CNNs, both are based on making feature sparsity learnable
with position-wise importance prediction. They are focal sparse convolution
(Focals Conv) and its multi-modal variant of focal sparse convolution with
fusion, or Focals Conv-F for short. The new modules can readily substitute
their plain counterparts in existing Sparse CNNs and be jointly trained in an
end-to-end fashion. For the first time, we show that spatially learnable
sparsity in sparse convolution is essential for sophisticated 3D object
detection. Extensive experiments on the KITTI, nuScenes and Waymo benchmarks
validate the effectiveness of our approach. Without bells and whistles, our
results outperform all existing single-model entries on the nuScenes test
benchmark at the paper submission time. Code and models are at
this https URL

本文提出了两个基于可学习特征稀疏性和位置重要性预测的新模块：焦点稀疏卷积和其多模式变体，可以很容易地替代现有稀疏卷积神经网络中的基本组件，并以端到端的方式联合训练，该新模块在 KITTI、nuScenes 和 Waymo 基准测试上进行了广泛实验，并在 nuScenes 测试基准中胜过了所有现有的单一模型条目。

基于 Focal Sparse 卷积网络的 3D 物体检测

Focal Sparse Convolutional Networks for 3D Object Detection

Convolutional network are the de-facto standard for analysing spatio-temporal
data such as images, videos, 3D shapes, etc. Whilst some of this data is
naturally dense (for instance, photos), many other data sources are inherently
sparse. Examples include pen-strokes forming on a piece of paper, or (colored)
3D point clouds that were obtained using a LiDAR scanner or RGB-D camera.
Standard "dense" implementations of convolutional networks are very inefficient
when applied on such sparse data. We introduce a sparse convolutional operation
tailored to processing sparse data that differs from prior work on sparse
convolutional networks in that it operates strictly on submanifolds, rather
than "dilating" the observation with every layer in the network. Our empirical
analysis of the resulting submanifold sparse convolutional networks shows that
they perform on par with state-of-the-art methods whilst requiring
substantially less computation.

文章提出了一种针对稀疏数据的稀疏卷积操作方法，不同于以前的稀疏卷积网络，其在处理子流形时非常高效，与最先进的方法性能相当，但需要明显更少的计算。