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Nowadays, the need for user editing in a 3D scene has rapidly increased dueto the development of AR and VR technology. However, the existing 3D scenecompletion task (and datasets) cannot suit the need because the missing regionsin scenes are generated by the sensor limitation or object occlusion. Thus, wepresent a novel task named free-form 3D scene inpainting. Unlike scenes inprevious 3D completion datasets preserving most of the main structures andhints of detailed shapes around missing regions, the proposed inpaintingdataset, FF-Matterport, contains large and diverse missing regions formed byour free-form 3D mask generation algorithm that can mimic human drawingtrajectories in 3D space. Moreover, prior 3D completion methods cannot performwell on this challenging yet practical task, simply interpolating nearbygeometry and color context. Thus, a tailored dual-stream GAN method isproposed. First, our dual-stream generator, fusing both geometry and colorinformation, produces distinct semantic boundaries and solves the interpolationissue. To further enhance the details, our lightweight dual-streamdiscriminator regularizes the geometry and color edges of the predicted scenesto be realistic and sharp. We conducted experiments with the proposedFF-Matterport dataset. Qualitative and quantitative results validate thesuperiority of our approach over existing scene completion methods and theefficacy of all proposed components.

Free-form 3D Scene Inpainting with Dual-stream GAN