  In response to the growing demand for 3D object detection in applications
such as autonomous driving, robotics, and augmented reality, this work focuses
on the evaluation of semi-supervised learning approaches for point cloud data.
The point cloud representation provides reliable and consistent observations
regardless of lighting conditions, thanks to advances in LiDAR sensors. Data
annotation is of paramount importance in the context of LiDAR applications, and
automating 3D data annotation with semi-supervised methods is a pivotal
challenge that promises to reduce the associated workload and facilitate the
emergence of cost-effective LiDAR solutions. Nevertheless, the task of
semi-supervised learning in the context of unordered point cloud data remains
formidable due to the inherent sparsity and incomplete shapes that hinder the
generation of accurate pseudo-labels. In this study, we consider these
challenges by posing the question: "To what extent does unlabelled data
contribute to the enhancement of model performance?" We show that improvements
from previous semi-supervised methods may not be as profound as previously
thought. Our results suggest that simple grid search hyperparameter tuning
applied to a supervised model can lead to state-of-the-art performance on the
ONCE dataset, while the contribution of unlabelled data appears to be
comparatively less exceptional.
