  SSF3D modified the semi-supervised 3D object detection (SS3DOD) framework,
which designed specifically for point cloud data. Leveraging the
characteristics of non-coincidence and weak correlation of target objects in
point cloud, we adopt a strategy of retaining only the truth-determining pseudo
labels and trimming the other fuzzy labels with points, instead of pursuing a
balance between the quantity and quality of pseudo labels. Besides, we notice
that changing the filter will make the model meet different distributed
targets, which is beneficial to break the training bottleneck. Two mechanism
are introduced to achieve above ideas: strict threshold and filter switching.
The experiments are conducted to analyze the effectiveness of above approaches
and their impact on the overall performance of the system. Evaluating on the
KITTI dataset, SSF3D exhibits superior performance compared to the current
state-of-the-art methods. The code will be released here.
