Semantic scene completion (SSC) is essential for achieving comprehensive perception in autonomous driving systems. However, existing SSC methods often overlook the high deployment costs in real-world applications. Traditional architectures, such as 3D Convolutional Neural Networks (3D CNNs) and self-attention mechanisms, face challenges in efficiently capturing long-range dependencies within 3D voxel grids, limiting their effectiveness. To address these issues, we introduce 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, aimed at exploring the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the perception training of a single vehicle using 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 that does not add extra model parameters, enabling efficient deployment. To further enhance the model's capability in capturing long-sequence relationships within 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments demonstrate that MetaSSC achieves state-of-the-art performance, significantly outperforming competing models while also reducing deployment costs.

翻译：语义场景补全（SSC）对于实现自动驾驶系统的全面感知至关重要。然而，现有SSC方法往往忽视了实际应用中的高部署成本。传统架构，如三维卷积神经网络（3D CNN）和自注意力机制，在高效捕获三维体素网格内的长程依赖关系方面面临挑战，限制了其有效性。为解决这些问题，我们提出了MetaSSC，一种新颖的基于元学习的SSC框架，它融合了可变形卷积、大核注意力以及Mamba（D-LKA-M）模型。我们的方法始于一个基于体素的语义分割（SS）预训练任务，旨在探索不完整区域的语义与几何特性，同时获取可迁移的元知识。利用模拟协同感知数据集，我们通过聚合来自附近多辆网联自动驾驶车辆（CAV）的传感器数据来监督单车的感知训练，从而生成更丰富、更全面的标签。随后，通过一种不增加额外模型参数的双阶段训练策略，将此元知识适配到目标域，实现高效部署。为进一步增强模型在三维体素网格内捕获长序列关系的能力，我们将Mamba模块与可变形卷积及大核注意力集成到骨干网络中。大量实验表明，MetaSSC实现了最先进的性能，显著优于竞争模型，同时降低了部署成本。