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Mapprior: Bird's-eye view perception with generative models
Zhu, Xiyue
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https://hdl.handle.net/2142/121554
Description
- Title
- Mapprior: Bird's-eye view perception with generative models
- Author(s)
- Zhu, Xiyue
- Issue Date
- 2023-07-21
- Director of Research (if dissertation) or Advisor (if thesis)
- Wang, Shenlong
- Kindratenko, Volodymyr
- Department of Study
- Electrical & Computer Eng
- Discipline
- Electrical & Computer Engr
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- BEV perception
- BEV map segmentation
- Generative models
- VQGAN
- Autonomous driving
- Abstract
- This thesis presents a novel generative methodology in bird’s-eye view map segmentation. Despite tremendous advancements in bird’s-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). This thesis introduces MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism, and uncertainty awareness. Evaluated on the large-scale nuScenes benchmark, it establishes a new state-of-the-art mean IoU score, with significantly improved MMD and ECE scores, for both cameras- and LiDAR-based BEV perception models. Furthermore, our method can be used to perpetually generate layouts with unconditional sampling.
- Graduation Semester
- 2023-08
- Type of Resource
- Thesis
- Copyright and License Information
- Copyright 2023 Xiyue Zhu
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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
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