  3D object detection in point clouds is important for autonomous driving
systems. A primary challenge in 3D object detection stems from the sparse
distribution of points within the 3D scene. Existing high-performance methods
typically employ 3D sparse convolutional neural networks with small kernels to
extract features. To reduce computational costs, these methods resort to
submanifold sparse convolutions, which prevent the information exchange among
spatially disconnected features. Some recent approaches have attempted to
address this problem by introducing large-kernel convolutions or self-attention
mechanisms, but they either achieve limited accuracy improvements or incur
excessive computational costs. We propose HEDNet, a hierarchical
encoder-decoder network for 3D object detection, which leverages
encoder-decoder blocks to capture long-range dependencies among features in the
spatial space, particularly for large and distant objects. We conducted
extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved
superior detection accuracy on both datasets than previous state-of-the-art
methods with competitive efficiency. The code is available at
https://github.com/zhanggang001/HEDNet.
