Deep Surface Normal Estimation on the 2-Sphere with Confidence Guided Semantic Attention

Abstract

We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with a low-quality depth channel. Unlike most previous works, we predict the normal on the 2-sphere rather than the 3D Euclidean space, which produces naturally normalized values and makes the training stable. Although the depth information is beneficial for normal estimation, the raw data contain missing values and noises. To alleviate this problem, we employ a confidence guided semantic attention (CGSA) module to progressively improve the quality of depth channel during training. The continuously refined depth features are fused with the normal features at multiple scales with the mutual feature fusion (MFF) modules to fully exploit the correlations between normals and depth, resulting in high quality normals and depth with fine details. Extensive experiments on multiple benchmark datasets prove the superiority of the proposed method.