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

Source: [LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation](<https://www.sota2.com/research/paper/68fa69cc1e209131cea24217>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:08:38.583Z

Authors: Peng Jiang, Srikanth Saripalli

Published: 2020-03-02T20:18:31.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.01174>)

[PDF](<https://arxiv.org/pdf/2003.01174.pdf>)