MILAN: Milli-Annotations for Lidar Semantic Segmentation
This reduces annotation costs for autonomous driving datasets, though it is incremental as it builds on existing self-supervised methods.
The paper tackles the high cost of annotating lidar point clouds for autonomous driving by using self-supervised representations to select informative scans and cluster points, achieving results comparable to fully-annotated training sets with only one thousandth of the point labels.
Annotating lidar point clouds for autonomous driving is a notoriously expensive and time-consuming task. In this work, we show that the quality of recent self-supervised lidar scan representations allows a great reduction of the annotation cost. Our method has two main steps. First, we show that self-supervised representations allow a simple and direct selection of highly informative lidar scans to annotate: training a network on these selected scans leads to much better results than a random selection of scans and, more interestingly, to results on par with selections made by SOTA active learning methods. In a second step, we leverage the same self-supervised representations to cluster points in our selected scans. Asking the annotator to classify each cluster, with a single click per cluster, then permits us to close the gap with fully-annotated training sets, while only requiring one thousandth of the point labels.