LISO: Lidar-only Self-Supervised 3D Object Detection
Stefan Baur Frank Moosmann Andreas Geiger
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.