Reconstruction based on the stereo camera has received considerable attention recently, but two particular challenges still remain. The first concerns the need to present and compress data in an effective way, and the second is to maintain as much of the available information as possible while ensuring sufficient accuracy. To overcome these issues, we propose a new 3D representation method, namely, planecell, that extracts planarity from the depth-assisted image segmentation and then directly projects these depth planes into the 3D world. The proposed method demonstrates its advancement especially dealing with large-scale structural environment, such as autonomous driving scene. The reconstruction result of our method achieves equal accuracy compared to dense point clouds and compresses the output file 200 times. To further obtain global surfaces, an energy function formulated from Conditional Random Field that generalizes the planar relationships is maximized. We evaluate our method with reconstruction baselines on the KITTI outdoor scene dataset, and the results indicate the superiorities compared to other 3D space representation methods in accuracy, memory requirements and the scope of applications.


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    Titel :

    Planecell: Representing Structural Space with Plane Elements


    Beteiligte:
    Fan, Lei (Autor:in) / Chen, Long (Autor:in) / Huang, Kai (Autor:in) / Cao, Dongpu (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    2096376 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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