/LiDARNet

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

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

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. Besides, we introduce a new dataset (SemanticUSL). 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.

Updates:

The paper is released on arXiv

The Code will come soon

Results

Experiment I: From SemanticKITTI to SemanticPOSS and SemanticUSL

From SemanticKITTI to SemanticPOSS and SemanticUSL

LiDARNetkitti



Experiment II: From SemanticPOSS to SemanticKITTI and SemanticUSL

From SemanticPOSS to SemanticKITTI and SemanticUSL

LiDARNetposs



Experiment III: From SemanticUSL to SemanticPOSS and SemanticKTTI

From SemanticPOSS to SemanticKITTI and SemanticUSL

LiDARNetusl

Citation

@misc{jiang2021lidarnet,
      title={LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation}, 
      author={Peng Jiang and Srikanth Saripalli},
      year={2021},
      eprint={2003.01174},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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