Manuel Dahnert, Angela Dai, Leonidas Guibas, Matthias Niessner. Joint Embedding of 3D Scan and CAD Objects. Int. Conf. Computer Vision (ICCV), 2019.

Abstract:

3D scan geometry and CAD models often contain complementary information towards understanding environments, which could be leveraged through establishing a mapping between the two domains. However, this is a challenging task due to strong, lower-level differences between scan and CAD geometry. We propose a novel approach to learn a joint embedding space between scan and CAD geometry, where semantically similar objects from both domains lie close together. To achieve this, we introduce a new 3D CNN-based approach to learn a joint embedding space representing object similarities across these domains. To learn a shared space where scan objects and CAD models can interlace, we propose a stacked hourglass approach to separate foreground and background from a scan object, and transform it to a complete, CAD-like representation to produce a shared embedding space. This embedding space can then be used for CAD model retrieval; to further enable this task, we introduce a new dataset of ranked scan-CAD similarity annotations, enabling new, fine-grained evaluation of CAD model retrieval to cluttered, noisy, partial scans. Our learned joint embedding outperforms current state of the art for CAD model retrieval by 12% in instance retrieval accuracy.

Bibtex: 

@inproceedings{ddgn-jesCAD-iccv2019,
    Author = {Manuel Dahnert and Angela Dai and Leonidas Guibas and Matthias Nie{ss}ner},
    Title = {Joint Embedding of 3D Scan and CAD Objects},
    Booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
    Year = {2019}
}