  The multi-line LiDAR is widely used in autonomous vehicles, so point
cloud-based 3D detectors are essential for autonomous driving. Extracting rich
multi-scale features is crucial for point cloud-based 3D detectors in
autonomous driving due to significant differences in the size of different
types of objects. However, because of the real-time requirements, large-size
convolution kernels are rarely used to extract large-scale features in the
backbone. Current 3D detectors commonly use feature pyramid networks to obtain
large-scale features; however, some objects containing fewer point clouds are
further lost during down-sampling, resulting in degraded performance. Since
pillar-based schemes require much less computation than voxel-based schemes,
they are more suitable for constructing real-time 3D detectors. Hence, we
propose the PillarNeXt, a pillar-based scheme. We redesigned the feature
encoding, the backbone, and the neck of the 3D detector. We propose the
Voxel2Pillar feature encoding, which uses a sparse convolution constructor to
construct pillars with richer point cloud features, especially height features.
The Voxel2Pillar adds more learnable parameters to the feature encoding,
enabling the initial pillars to have higher performance ability. We extract
multi-scale and large-scale features in the proposed fully sparse backbone,
which does not utilize large-size convolutional kernels; the backbone consists
of the proposed multi-scale feature extraction module. The neck consists of the
proposed sparse ConvNeXt, whose simple structure significantly improves the
performance. We validate the effectiveness of the proposed PillarNeXt on the
Waymo Open Dataset, and the object detection accuracy for vehicles,
pedestrians, and cyclists is improved. We also verify the effectiveness of each
proposed module in detail through ablation studies.
