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Semantic scene completion (SSC) is a challenging Computer Vision task withmany practical applications, from robotics to assistive computing. Its goal isto infer the 3D geometry in a field of view of a scene and the semantic labelsof voxels, including occluded regions. In this work, we present SPAwN, a novellightweight multimodal 3D deep CNN that seamlessly fuses structural data fromthe depth component of RGB-D images with semantic priors from a bimodal 2Dsegmentation network. A crucial difficulty in this field is the lack of fullylabeled real-world 3D datasets which are large enough to train the currentdata-hungry deep 3D CNNs. In 2D computer vision tasks, many data augmentationstrategies have been proposed to improve the generalization ability of CNNs.However those approaches cannot be directly applied to the RGB-D input andoutput volume of SSC solutions. In this paper, we introduce the use of a 3Ddata augmentation strategy that can be applied to multimodal SSC networks. Wevalidate our contributions with a comprehensive and reproducible ablationstudy. Our solution consistently surpasses previous works with a similar levelof complexity.

Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors