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In this paper, a method for dense semantic 3D scene reconstruction from anRGB-D sequence is proposed to solve high-level scene understanding tasks.First, each RGB-D pair is consistently segmented into 2D semantic maps based ona camera tracking backbone that propagates objects' labels with highprobabilities from full scans to corresponding ones of partial views. Then adense 3D mesh model of an unknown environment is incrementally generated fromthe input RGB-D sequence. Benefiting from 2D consistent semantic segments andthe 3D model, a novel semantic projection block (SP-Block) is proposed toextract deep feature volumes from 2D segments of different views. Moreover, thesemantic volumes are fused into deep volumes from a point cloud encoder to makethe final semantic segmentation. Extensive experimental evaluations on publicdatasets show that our system achieves accurate 3D dense reconstruction andstate-of-the-art semantic prediction performances simultaneously.

Semantic Dense Reconstruction with Consistent Scene Segments