@article{Qu2025, 
author = {Yansong Qu and Zixuan Xu and Zilin Huang and Zihao Sheng and Sikai Chen and Tiantian Chen},
title = {MetaSSC: Enhancing 3D semantic scene completion for autonomous driving through meta-learning and long-sequence modeling},
year = {2025},
journal = {Communications in Transportation Research},
volume = {5},
number = {2},
pages = {100184},
keywords = {Three-dimensional (3D) semantic scene completion, Autonomous driving, Meta-learning, Deformable large-kernel attention, State space model},
url = {https://www.sciopen.com/article/10.1016/j.commtr.2025.100184},
doi = {10.1016/j.commtr.2025.100184},
abstract = {Semantic scene completion (SSC) plays a pivotal role in achieving comprehensive perceptions of autonomous driving systems. However, existing methods often neglect the high deployment costs of SSC in real-world applications, and traditional architectures such as three-dimensional (3D) convolutional neural networks (3D CNNs) and self-attention mechanisms struggle to efficiently capture long-range dependencies within 3D voxel grids, limiting their effectiveness. To address these challenges, we propose MetaSSC, a novel meta-learning-based framework for SSC that leverages deformable convolution, large-kernel attention, and the Mamba (D-LKA-M) model. Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, which is designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle's perception via the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model's ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin while also reducing deployment costs.}
}