DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception

• URL: http://arxiv.org/abs/2401.08687v1
• Date: Sat, 13 Jan 2024 04:21:24 GMT
• Title: DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
• Authors: Kai Jiang, Jiaxing Huang, Weiying Xie, Yunsong Li, Ling Shao, Shijian Lu
• Abstract summary: Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. We design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features.
• Score: 111.13119809216313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup which cannot scale well while handling various new data. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. In this work, we design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features. DA-BEV introduces the idea of query into the domain adaptation framework to derive useful information from image-view and BEV features. It consists of two query-based designs, namely, query-based adversarial learning (QAL) and query-based self-training (QST), which exploits image-view features or BEV features to regularize the adaptation of the other. Extensive experiments show that DA-BEV achieves superior domain adaptive BEV perception performance consistently across multiple datasets and tasks such as 3D object detection and 3D scene segmentation.

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