  We propose 4 insights that help to significantly improve the performance of
deep learning models that predict surface normals and semantic labels from a
single RGB image. These insights are: (1) denoise the "ground truth" surface
normals in the training set to ensure consistency with the semantic labels; (2)
concurrently train on a mix of real and synthetic data, instead of pretraining
on synthetic and finetuning on real; (3) jointly predict normals and semantics
using a shared model, but only backpropagate errors on pixels that have valid
training labels; (4) slim down the model and use grayscale instead of color
inputs. Despite the simplicity of these steps, we demonstrate consistently
improved results on several datasets, using a model that runs at 12 fps on a
standard mobile phone.
