Abstract
Traditional urban reconstruction methods can only output incomplete 3D models, which depict the scene regions that are visible to the moving camera. While learning-based shape reconstruction techniques make single-view 3D reconstruction possible, they are designed to handle single objects that are well-presented in the training datasets. This paper presents a novel learning-based approach for reconstructing complete 3D meshes for large-scale urban scenes in real-time. The input video sequences are fed into a localization module, which segments different objects and determines their relative positions. Each object is then reconstructed under their local coordinates to better approximate models in the training datasets. The reconstruction module is adopted from BSP-Net (Chen et al., 2020), which is capable of producing compact polygon meshes. However, major changes have been made so that unoriented objects in large-scale scenes can be reconstructed efficiently using only a small number of planes. Experimental results demonstrate that our approach can reconstruct urban scenes with buildings and vehicles using 400∼800 convex parts in 0.1∼0.5 s.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.