Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to reduce the enormous burden of 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., smashed data) to preserve privacy. Since these intermediate features are naturally produced by the inference pipeline, no additional computation is required on autonomous vehicles. However, generating effective consistency loss for unlabeled feature-level scene turns out to be a critical challenge. The latest SSL frameworks for 3D object detection that enforce consistency regularization between different augmentations of an unlabeled raw-point scene become detrimental when applied to intermediate features. To solve the problem, we introduce a novel combination of hybrid pseudo labels and feature-level Ground Truth sampling (F-GT), which safely augments unlabeled multi-type 3D scene features and provides high-quality supervision. We implement UpCycling on two representative 3D object detection models: SECOND-IoU and PV-RCNN. Experiments on widely-used datasets (Waymo, KITTI, and Lyft) verify that UpCycling outperforms other augmentation methods applied at the feature level. In addition, while preserving privacy, UpCycling performs better or comparably to the state-of-the-art methods that utilize raw-level unlabeled data in both domain adaptation and partial-label scenarios.

翻译：半监督学习（SSL）在自动驾驶领域日益受到关注，旨在减轻3D标注的巨大负担。本文提出UpCycling——一种新颖的零额外原始点云半监督3D目标检测框架，通过利用未标注的去标识化中间特征（即粉碎数据）进行学习以保护隐私。由于这些中间特征由推理管线天然生成，无需在自动驾驶车辆上增加额外计算。然而，为未标注特征级场景生成有效的一致性损失成为关键挑战。最新面向3D目标检测的半监督框架通过对未标注原始点云场景的不同增强实施一致性正则化，但直接应用于中间特征时会产生不利影响。为解决该问题，我们引入混合伪标签与特征级真实数据采样（F-GT）的创新组合方法，既能安全增强未标注的多类型3D场景特征，又能提供高质量监督信号。我们在两个代表性3D目标检测模型——SECOND-IoU和PV-RCNN上实现UpCycling。在广泛使用的数据集（Waymo、KITTI和Lyft）上的实验验证表明，UpCycling优于其他特征级增强方法。此外，在保持隐私的同时，UpCycling在领域自适应和部分标签场景中的性能达到或超越利用原始级未标注数据的现有最优方法。