A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distillation to bridge the gap between a model trained on high-quality inputs at training time and another tested on low-quality inputs at inference time. In particular, we design a two-stage training pipeline for point cloud object detection. First, we train an object detection model on dense point clouds, which are generated from multiple frames using extra information only available at training time. Then, we train the model's identical counterpart on sparse single-frame point clouds with consistency regularization on features from both models. We show that this procedure improves performance on low-quality data during testing, without additional overhead.

中文翻译：

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多帧到单帧：用于 3D 对象检测的知识提炼

自动驾驶 3D 对象检测的一个常见困境是高质量、密集的点云仅在训练期间可用，而在测试期间不可用。我们使用知识蒸馏来弥合在训练时在高质量输入上训练的模型与在推理时在低质量输入上测试的模型之间的差距。特别是，我们设计了一个用于点云对象检测的两阶段训练管道。首先，我们在密集点云上训练对象检测模型，这些模型是使用仅在训练时可用的额外信息从多帧生成的。然后，我们在稀疏的单帧点云上训练模型的相同副本，并对两个模型的特征进行一致性正则化。我们表明，此过程可在测试期间提高低质量数据的性能，而无需额外开销。