MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
This addresses the scalability issue for autonomous vehicles by enabling data reuse and reducing labeling efforts, though it is incremental as it builds on existing novel view synthesis methods.
The paper tackles the problem of cross-sensor configuration generalization in online mapping for autonomous vehicles by using novel view synthesis with Gaussian splatting to augment training data, resulting in an 18% performance improvement, faster convergence, and state-of-the-art performance with only 25% of the original training data.
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sensor configuration generalization, leading to performance degradation when models are deployed on vehicles with different camera intrinsics and extrinsics. With the rapid evolution of novel view synthesis methods, we investigate the extent to which these techniques can be leveraged to address the sensor configuration generalization challenge. We propose a novel framework leveraging Gaussian splatting to reconstruct scenes and render camera images in target sensor configurations. The target config sensor data, along with labels mapped to the target config, are used to train online mapping models. Our proposed framework on the nuScenes and Argoverse 2 datasets demonstrates a performance improvement of 18% through effective dataset augmentation, achieves faster convergence and efficient training, and exceeds state-of-the-art performance when using only 25% of the original training data. This enables data reuse and reduces the need for laborious data labeling. Project page at https://henryzhangzhy.github.io/mapgs.