LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

• URL: http://arxiv.org/abs/2003.01174v3
• Date: Sat, 24 Apr 2021 21:32:21 GMT
• Title: LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
• Authors: Peng Jiang and Srikanth Saripalli
• Abstract summary: We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet) Our model can extract both the domain private features and the domain shared features with a two-branch structure. Using our approach, we can get a single projection-based LiDAR full-scene semantic segmentation model working on both domains.
• Score: 18.211513930388417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two-branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the domain shared and private features. Additionally, we introduce a new dataset (SemanticUSL\footnote{The access address of SemanticUSL:\url{https://unmannedlab.github.io/research/SemanticUSL}}) for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. We conducted experiments on real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have differences in channel distributions, reflectivity distributions, diversity of scenes, and sensors setup. Using our approach, we can get a single projection-based LiDAR full-scene semantic segmentation model working on both domains. Our model can keep almost the same performance on the source domain after adaptation and get an 8\%-22\% mIoU performance increase in the target domain.

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