  Semi-supervised 3D object detection is a promising yet under-explored
direction to reduce data annotation costs, especially for cluttered indoor
scenes. A few prior works, such as SESS and 3DIoUMatch, attempt to solve this
task by utilizing a teacher model to generate pseudo-labels for unlabeled
samples. However, the availability of unlabeled samples in the 3D domain is
relatively limited compared to its 2D counterpart due to the greater effort
required to collect 3D data. Moreover, the loose consistency regularization in
SESS and restricted pseudo-label selection strategy in 3DIoUMatch lead to
either low-quality supervision or a limited amount of pseudo labels. To address
these issues, we present a novel Dual-Perspective Knowledge Enrichment approach
named DPKE for semi-supervised 3D object detection. Our DPKE enriches the
knowledge of limited training data, particularly unlabeled data, from two
perspectives: data-perspective and feature-perspective. Specifically, from the
data-perspective, we propose a class-probabilistic data augmentation method
that augments the input data with additional instances based on the varying
distribution of class probabilities. Our DPKE achieves feature-perspective
knowledge enrichment by designing a geometry-aware feature matching method that
regularizes feature-level similarity between object proposals from the student
and teacher models. Extensive experiments on the two benchmark datasets
demonstrate that our DPKE achieves superior performance over existing
state-of-the-art approaches under various label ratio conditions. The source
code will be made available to the public.
