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Indoor scene semantic parsing from RGB images is very challenging due toocclusions, object distortion, and viewpoint variations. Going beyond priorworks that leverage geometry information, typically paired depth maps, wepresent a new approach, a 3D-to-2D distillation framework, that enables us toleverage 3D features extracted from large-scale 3D data repository (e.g.,ScanNet-v2) to enhance 2D features extracted from RGB images. Our work hasthree novel contributions. First, we distill 3D knowledge from a pretrained 3Dnetwork to supervise a 2D network to learn simulated 3D features from 2Dfeatures during the training, so the 2D network can infer without requiring 3Ddata. Second, we design a two-stage dimension normalization scheme to calibratethe 2D and 3D features for better integration. Third, we design asemantic-aware adversarial training model to extend our framework for trainingwith unpaired 3D data. Extensive experiments on various datasets, ScanNet-V2,S3DIS, and NYU-v2, demonstrate the superiority of our approach. Also,experimental results show that our 3D-to-2D distillation improves the modelgeneralization.

3D-to-2D Distillation for Indoor Scene Parsing