MapGS: 新規視点合成によるオンラインマッピングの汎用事前学習とデータ拡張
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Gaussian Splattingでシーンを再構成し、異なるセンサ構成のカメラ画像を合成してオンラインマッピングモデルを学習させることで、センサ構成の違いへの汎化性能を向上させた研究。
著者: Hengyuan Zhang, David Paz, Yuliang Guo, Xinyu Huang, Henrik I. Christensen, Liu Ren
分類: cs.CV, cs.RO
原文アブストラクト
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.
