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。