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.
@article{arxiv.1906.06792,
title = {Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction},
author = {Steven Hickson and Karthik Raveendran and Alireza Fathi and Kevin Murphy and Irfan Essa},
journal= {arXiv preprint arXiv:1906.06792},
year = {2019}
}