Street-View Image Generation from a Bird's-Eye View Layout
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910327827857408 |
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| author | Swerdlow, Alexander Xu, Runsheng Zhou, Bolei |
| author_facet | Swerdlow, Alexander Xu, Runsheng Zhou, Bolei |
| contents | Bird's-Eye View (BEV) Perception has received increasing attention in recent years as it provides a concise and unified spatial representation across views and benefits a diverse set of downstream driving applications. At the same time, data-driven simulation for autonomous driving has been a focal point of recent research but with few approaches that are both fully data-driven and controllable. Instead of using perception data from real-life scenarios, an ideal model for simulation would generate realistic street-view images that align with a given HD map and traffic layout, a task that is critical for visualizing complex traffic scenarios and developing robust perception models for autonomous driving. In this paper, we propose BEVGen, a conditional generative model that synthesizes a set of realistic and spatially consistent surrounding images that match the BEV layout of a traffic scenario. BEVGen incorporates a novel cross-view transformation with spatial attention design which learns the relationship between cameras and map views to ensure their consistency. We evaluate the proposed model on the challenging NuScenes and Argoverse 2 datasets. After training, BEVGen can accurately render road and lane lines, as well as generate traffic scenes with diverse different weather conditions and times of day. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2301_04634 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Street-View Image Generation from a Bird's-Eye View Layout Swerdlow, Alexander Xu, Runsheng Zhou, Bolei Computer Vision and Pattern Recognition Bird's-Eye View (BEV) Perception has received increasing attention in recent years as it provides a concise and unified spatial representation across views and benefits a diverse set of downstream driving applications. At the same time, data-driven simulation for autonomous driving has been a focal point of recent research but with few approaches that are both fully data-driven and controllable. Instead of using perception data from real-life scenarios, an ideal model for simulation would generate realistic street-view images that align with a given HD map and traffic layout, a task that is critical for visualizing complex traffic scenarios and developing robust perception models for autonomous driving. In this paper, we propose BEVGen, a conditional generative model that synthesizes a set of realistic and spatially consistent surrounding images that match the BEV layout of a traffic scenario. BEVGen incorporates a novel cross-view transformation with spatial attention design which learns the relationship between cameras and map views to ensure their consistency. We evaluate the proposed model on the challenging NuScenes and Argoverse 2 datasets. After training, BEVGen can accurately render road and lane lines, as well as generate traffic scenes with diverse different weather conditions and times of day. |
| title | Street-View Image Generation from a Bird's-Eye View Layout |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2301.04634 |