surface normal loss
时间: 2023-10-05 07:12:14 浏览: 23
Surface normal loss is a type of loss function used in computer vision tasks such as 3D object recognition and image segmentation. The goal of the loss function is to improve the accuracy of predicting surface normals, which are vectors perpendicular to a surface at a given point.
The surface normal loss function is typically used as a regularization term in a neural network. It is computed by comparing the predicted surface normals to the ground truth surface normals and penalizing the difference between them. This encourages the model to learn features that are consistent with the underlying surface structure of the object being analyzed.
In summary, surface normal loss is a way to improve the accuracy of predicting surface normals in computer vision tasks by penalizing the difference between predicted and ground truth surface normals.