This paper shows the effectiveness of 2D backbone scaling and pretraining for
pillar-based 3D object detectors. Pillar-based methods mainly employ randomly
initialized 2D convolution neural network (ConvNet) for feature extraction and
fail to enjoy the benefits from the backbone scaling and pretraining in the
image domain. To show the scaling-up capacity in point clouds, we introduce the
dense ConvNet pretrained on large-scale image datasets (e.g., ImageNet) as the
2D backbone of pillar-based detectors. The ConvNets are adaptively designed
based on the model size according to the specific features of point clouds,
such as sparsity and irregularity. Equipped with the pretrained ConvNets, our
proposed pillar-based detector, termed PillarNeSt, outperforms the existing 3D
object detectors by a large margin on the nuScenes and Argoversev2 datasets.
Our code shall be released upon acceptance.

本研究论文展示了 2D 背骨缩放和预训练对基于 pillar 的三维物体探测器的有效性，通过引入在大规模图像数据集上预训练的密集 ConvNet 作为 pillar-based 探测器的 2D 背骨，基于模型大小自适应设计的 ConvNets 可根据点云的特征（如稀疏性和不规则性）发挥其在点云领域的扩展能力，我们提出的 PillarNeSt 在 nuScenes 和 Argoversev2 数据集上远远超过现有的 3D 物体探测器。