Surface Normal Prediction via Pair-Wise Angular Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional surface normal prediction from single images using pixel-wise regression is limited by ambiguity in camera-centric coordinate systems, making it difficult to leverage diverse training data from different sources and camera setups, leading to poor generalization and accuracy.
Innovation Solution
The technology formulates surface normal prediction as a pair-wise angular regression task using a pair-wise angular loss function, incorporating additional loss functions like up-vector enforcing and rotation invariant gradient losses to improve robustness and accuracy, and employs a refinement model to upsample normal maps to higher resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pixel-wise regression is used for surface normal prediction, then the model can be trained on diverse training data from different camera setups, but the prediction accuracy and generalization are poor due to coordinate system ambiguity
Solution Approach 1:
The patent changes the prediction target from absolute normal vectors (which are ambiguous in camera-centric coordinate systems) to relative angular values between pairs of normal vectors. This parameter transformation eliminates coordinate system dependency while preserving geometric relationships, enabling both diverse data utilization and accurate prediction.
Solution Approach 2:
The patent introduces angular values as an intermediary representation between the input image and the final normal vector prediction. By first predicting relative angles between normal vectors and then converting these angles to absolute orientations using a reference normal, the system achieves coordinate-invariant training with accurate absolute predictions.
2Ease of manufacture
If conventional loss functions are used for training, then the training process is simple, but the model fails to capture geometric relationships between normal vectors leading to poor generalization
Solution Approach 1:
The patent transforms the loss function from operating on absolute normal vector components to operating on relative angular values. This parameter change in the loss computation enables the model to learn geometric relationships that are invariant to coordinate system transformations, significantly improving generalization while maintaining training feasibility.
3Measurement precision
If normal maps are predicted at high resolution directly, then the output quality is high, but the computational complexity and training difficulty increase significantly
Solution Approach 1:
The patent segments the surface normal prediction task into two independent sub-tasks: (1) predicting relative angular values between normal vectors, and (2) converting these angles to absolute normal vectors using a reference. This segmentation allows each sub-task to be optimized independently, reducing overall computational complexity while maintaining high output quality.
Data Source
AI summary
A surface normal model is trained to predict normal maps from single images using pair-wise angular losses. A training dataset comprising a training image and a ground truth normal map for the training image is received. To train the surface normal model using the training dataset, a predicted normal map is generated for the training image using the surface normal model. A loss is determined as a function of angular values between pairs of normal vectors for the predicted normal map and corresponding angular values between pairs of normal vectors for the ground truth normal map. The surface normal model is updated based on the loss.