  3D semantic scene labeling is a fundamental task for Autonomous Driving.
Recent work shows the capability of Deep Neural Networks in labeling 3D point
sets provided by sensors like LiDAR, and Radar. Imbalanced distribution of
classes in the dataset is one of the challenges that face 3D semantic scene
labeling task. This leads to misclassifying for the non-dominant classes which
suffer from two main problems: a) rare appearance in the dataset, and b) few
sensor points reflected from one object of these classes. This paper proposes a
Weighted Self-Incremental Transfer Learning as a generalized methodology that
solves the imbalanced training dataset problems. It re-weights the components
of the loss function computed from individual classes based on their
frequencies in the training dataset, and applies Self-Incremental Transfer
Learning by running the Neural Network model on non-dominant classes first,
then dominant classes one-by-one are added. The experimental results introduce
a new 3D point cloud semantic segmentation benchmark for KITTI dataset.
