  Indoor scene semantic parsing from RGB images is very challenging due to
occlusions, object distortion, and viewpoint variations. Going beyond prior
works that leverage geometry information, typically paired depth maps, we
present a new approach, a 3D-to-2D distillation framework, that enables us to
leverage 3D features extracted from large-scale 3D data repository (e.g.,
ScanNet-v2) to enhance 2D features extracted from RGB images. Our work has
three novel contributions. First, we distill 3D knowledge from a pretrained 3D
network to supervise a 2D network to learn simulated 3D features from 2D
features during the training, so the 2D network can infer without requiring 3D
data. Second, we design a two-stage dimension normalization scheme to calibrate
the 2D and 3D features for better integration. Third, we design a
semantic-aware adversarial training model to extend our framework for training
with 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 model
generalization.
