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This paper studies the problem of holistic 3D wireframe perception (HoW-3D),a new task of perceiving both the visible 3D wireframes and the invisible onesfrom single-view 2D images. As the non-front surfaces of an object cannot bedirectly observed in a single view, estimating the non-line-of-sight (NLOS)geometries in HoW-3D is a fundamentally challenging problem and remains open incomputer vision. We study the problem of HoW-3D by proposing an ABC-HoWbenchmark, which is created on top of CAD models sourced from the ABC-datasetwith 12k single-view images and the corresponding holistic 3D wireframe models.With our large-scale ABC-HoW benchmark available, we present a novel DeepSpatial Gestalt (DSG) model to learn the visible junctions and line segments asthe basis and then infer the NLOS 3D structures from the visible cues byfollowing the Gestalt principles of human vision systems. In our experiments,we demonstrate that our DSG model performs very well in inferring the holistic3D wireframes from single-view images. Compared with the strong baselinemethods, our DSG model outperforms the previous wireframe detectors indetecting the invisible line geometry in single-view images and is even verycompetitive with prior arts that take high-fidelity PointCloud as inputs onreconstructing 3D wireframes.

HoW-3D: Holistic 3D Wireframe Perception from a Single Image