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

arXiv: https://arxiv.org/abs/2003.01174 · HTML: https://mlsanity.com/paper/2003.01174/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View](https://mlsanity.com/paper/2104.11021/) (2021)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [Sensor Equivariance by LiDAR Projection Images](https://mlsanity.com/paper/2305.00221/) (2023)
- [Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans](https://mlsanity.com/paper/2504.21602/) (2025)
- [LiDAR Based Semantic Perception for Forklifts in Outdoor Environments](https://mlsanity.com/paper/2505.22258/) (2025)
- [Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning](https://mlsanity.com/paper/2201.05585/) (2022)
- [Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments](https://mlsanity.com/paper/2506.09552/) (2025)

## Abstract (arXiv)

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 (SemanticUSLfootnoteThe access address of SemanticUSL:urlhttps://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.