  MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the
dense geometry and semantics of a scene are inferred from a single monocular
RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we
solve the complex problem of 2D to 3D scene reconstruction while jointly
inferring its semantics. Our framework relies on successive 2D and 3D UNets
bridged by a novel 2D-3D features projection inspiring from optics and
introduces a 3D context relation prior to enforce spatio-semantic consistency.
Along with architectural contributions, we introduce novel global scene and
local frustums losses. Experiments show we outperform the literature on all
metrics and datasets while hallucinating plausible scenery even beyond the
camera field of view. Our code and trained models are available at
https://github.com/cv-rits/MonoScene.
