LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic Segmentation
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We present a boundary-aware domain adaptation model for Lidar point cloud semantic segmentation. Our model is designed to extract both the domain private features and the domain shared features using shared weight. We embedded Gated-SCNN into the shared features extractors to help it learn boundary information while learning other shared features. Besides, the CycleGAN mechanism is imposed for further adaptation. We conducted experiments on real-world datasets. The source domain data is from the Semantic KITTI dataset, and the target domain data is collected from our own platform (a warthog) in off-road as well as urban scenarios. The two datasets have differences in channel distributions, reflectivity distributions, and sensors setup. Using our approach, we are able to get a single model that can work on both domains. The model is capable of achieving the state of art performance on the source domain (Semantic KITTI dataset) and get 44.0% mIoU on the target domain dataset.
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