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Systems which incrementally create 3D semantic maps from image sequences muststore and update representations of both geometry and semantic entities.However, while there has been much work on the correct formulation forgeometrical estimation, state-of-the-art systems usually rely on simplesemantic representations which store and update independent label estimates foreach surface element (depth pixels, surfels, or voxels). Spatial correlation isdiscarded, and fused label maps are incoherent and noisy.  We introduce a new compact and optimisable semantic representation bytraining a variational auto-encoder that is conditioned on a colour image.Using this learned latent space, we can tackle semantic label fusion by jointlyoptimising the low-dimenional codes associated with each of a set ofoverlapping images, producing consistent fused label maps which preservespatial correlation. We also show how this approach can be used within amonocular keyframe based semantic mapping system where a similar code approachis used for geometry. The probabilistic formulation allows a flexibleformulation where we can jointly estimate motion, geometry and semantics in aunified optimisation.

SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations