End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
This addresses a critical challenge in autonomous driving by enhancing segmentation accuracy for rare classes, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of imbalanced class distribution in 3D semantic segmentation for autonomous driving by proposing a Weighted Self-Incremental Transfer Learning method, which improves performance on non-dominant classes and establishes a new benchmark on the KITTI dataset.
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.