3D object detection is one of the most important components in any
Self-Driving stack, but current state-of-the-art (SOTA) lidar object detectors
require costly & slow manual annotation of 3D bounding boxes to perform well.
Recently, several methods emerged to generate pseudo ground truth without human
supervision, however, all of these methods have various drawbacks: Some methods
require sensor rigs with full camera coverage and accurate calibration, partly
supplemented by an auxiliary optical flow engine. Others require expensive
high-precision localization to find objects that disappeared over multiple
drives. We introduce a novel self-supervised method to train SOTA lidar object
detection networks which works on unlabeled sequences of lidar point clouds
only, which we call trajectory-regularized self-training. It utilizes a SOTA
self-supervised lidar scene flow network under the hood to generate, track, and
iteratively refine pseudo ground truth. We demonstrate the effectiveness of our
approach for multiple SOTA object detection networks across multiple real-world
datasets. Code will be released.

自动驾驶堆栈中，3D 物体检测是最重要的组成部分之一，但当前最先进的激光雷达物体检测器需要昂贵而缓慢的人工注释才能取得良好性能。我们引入了一种自我监督方法来训练 SOTA 激光雷达物体检测网络，该方法仅基于未标记的激光点云序列工作，我们称之为轨迹规范化自我训练。我们演示了我们的方法对于多个 SOTA 物体检测网络在多个真实世界数据集上的有效性。