Summary of Metassc: Enhancing 3d Semantic Scene Completion For Autonomous Driving Through Meta-learning and Long-sequence Modeling, by Yansong Qu et al.
MetaSSC: Enhancing 3D Semantic Scene Completion for Autonomous Driving through Meta-Learning and Long-sequence Modeling
by Yansong Qu, Zixuan Xu, Zilin Huang, Zihao Sheng, Tiantian Chen, Sikai Chen
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel meta-learning-based framework for semantic scene completion (SSC), dubbed MetaSSC. The goal is to enable efficient and effective SSC in autonomous driving systems, addressing the challenges posed by traditional architectures like 3D Convolutional Neural Networks (3D CNNs) and self-attention mechanisms. MetaSSC leverages deformable convolution, large-kernel attention, and the Mamba (D-LKA-M) model, pretraining with a voxel-based semantic segmentation task to acquire transferable meta-knowledge. This knowledge is then adapted to the target domain using a dual-phase training strategy, without adding extra model parameters. The paper also integrates Mamba blocks with deformable convolution and large-kernel attention into the backbone network to capture long-sequence relationships within 3D voxel grids. Extensive experiments demonstrate that MetaSSC achieves state-of-the-art performance in SSC, significantly outperforming competing models while reducing deployment costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make cars smarter! It’s all about filling in missing parts of a scene, like what’s behind a building or in another room. Right now, computers are pretty bad at this task. The authors created a new way to do it using “meta-learning” and special computer tricks. They tested their idea on pretend scenarios where multiple cars share information with each other, which helped the computer learn even better. This could make self-driving cars more accurate and able to understand what’s going on around them. |
Keywords
» Artificial intelligence » Attention » Meta learning » Pretraining » Self attention » Semantic segmentation
