Point cloud representation has recently become a research hotspot in the field of computer vision and has been utilized for autonomous vehicles. However, adapting deep learning networks for point cloud data recognition is challenging due to the variability in datasets and sensor technologies. This variability underscores the necessity for adaptive techniques to maintain accuracy under different conditions. In this paper, we present the Multi-View Structural Convolution Network (MSCN) designed for domain-invariant point cloud recognition. MSCN comprises Structural Convolution Layers (SCL) that extract local context geometric features from point clouds and Structural Aggregation Layers (SAL) that extract and aggregate both local and overall context features from point clouds. Additionally, our MSCN enhances feature representation robustness by training with unseen domain point clouds derived from source domain point clouds. This method acquires domain-invariant features and exhibits robust, consistent performance across various point cloud datasets, ensuring compatibility with diverse sensor configurations without the need for parameter adjustments. This highlights MSCN's potential to significantly improve the reliability and domain invariant features in different environments. Our code is available atthis https URL.
View on arXiv@article{kim2025_2501.16289,
title={ Multi-view Structural Convolution Network for Domain-Invariant Point Cloud Recognition of Autonomous Vehicles },
author={ Younggun Kim and Beomsik Cho and Seonghoon Ryoo and Soomok Lee },
journal={arXiv preprint arXiv:2501.16289},
year={ 2025 }
}