Papers › Self-supervised Learning for Single View Depth and Surface Normal Estimation

Self-supervised Learning for Single View Depth and Surface Normal Estimation

1 Mar 2019arXiv:1903.00112archive 2025-07-28

Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg, Ian Reid

In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image. In contrast to most existing frameworks which represent outdoor scenes as fronto-parallel planes at piece-wise smooth depth, we propose to predict depth with surface orientation while assuming that natural scenes have piece-wise smooth normals. We show that a simple depth-normal consistency as a soft-constraint on the predictions is sufficient and effective for training both these networks simultaneously. The trained normal network provides state-of-the-art predictions while the depth network, relying on much realistic smooth normal assumption, outperforms the traditional self-supervised depth prediction network by a large margin on the KITTI benchmark. Demo video: https://youtu.be/ZD-ZRsw7hdM

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