MIM4D: Masked Modeling for Autonomous Driving Representation Learning
Belangrijkste concepten
MIM4D proposes a novel pre-training paradigm based on dual masked image modeling (MIM) for autonomous driving representation learning, achieving state-of-the-art performance on the nuScenes dataset.
Samenvatting
Introduction to the challenge of learning visual representations in autonomous driving.
Existing pre-training methods categorized into depth-supervised and NeRF-based methods.
Proposal of MIM4D as a novel pre-training paradigm leveraging spatial and temporal relations.
Detailed explanation of the architecture and methodology of MIM4D.
Extensive experiments demonstrating the effectiveness of MIM4D across various downstream tasks.
Comparison with previous pre-training methods and state-of-the-art approaches.
Ablation studies to analyze the impact of different components in the model.
Conclusion highlighting the contributions and effectiveness of MIM4D in scalable autonomous driving representation learning.