Pixel Perspective Refinement Using Global Image Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current state-of-the-art models for per-pixel coordinate estimation, such as FrameNet, fail to accurately estimate principal directions for pixels in both indoor and outdoor images, particularly due to a lack of global correlation based on image gravity, leading to noisy placements of objects in planar regions.
Innovation Solution
A neural network framework that determines three-dimensional principal directions for each pixel, incorporating global normalization and using two-dimensional vanishing point estimation to refine pixel directions, enabling accurate estimation for both indoor and outdoor images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pixel-wise local information is used for principal direction estimation, then the model can process images efficiently, but the placement of objects inside planar regions becomes noisy with jittering
Solution Approach 1:
The patent combines local pixel-wise information with global image gravity information to estimate principal directions. The system integrates both approaches by using local normal directions from the image and global gravity direction from segmented horizontal/vertical regions, merging them through a refinement module that produces more accurate and stable principal direction estimates without sacrificing processing efficiency
Solution Approach 2:
The patent introduces global normalization information as an intermediary element that mediates between local pixel information and global image structure. The refinement module acts as an intermediary process that takes local normal directions and refines them using global gravity information, eliminating jittering while maintaining processing efficiency
2Ease of manufacture
If datasets with fixed environment are used for training, then data collection is easy from indoor environments, but the model cannot accurately estimate principal directions for outdoor and nature images
Solution Approach 1:
The patent segments the image into horizontal and vertical regions based on normal directions to identify global gravity information. This segmentation approach allows the model to extract structural information from diverse image types (indoor, outdoor, nature) by identifying common geometric patterns, thereby improving adaptability while maintaining the ability to process various environments
Solution Approach 2:
The patent changes the training approach by using parameter-free geometric reasoning based on image gravity and vanishing points. Instead of relying on environment-specific datasets, the system uses universal geometric properties (horizontal/vertical region segmentation, gravity direction estimation) that can be applied across all image types, enabling the model to generalize from indoor training data to outdoor and nature images
Data Source
AI summary
A method and network for refining pixels in an image are provided. The method includes determining principle directions of a pixel in the image and determining a normal direction of the pixel in the image. The method further includes segmenting regions in the image as one or more horizontal regions and one or more vertical regions based on the normal direction of the pixel and obtaining global normalization information for the image from the segmented regions. The method also includes refining the principle directions of the pixel in the image based on the global normalization information. A method is also provided for determining perspective directions of pixels in the image.