Indoor Image Inpainting via Multimodal Structural Guidance
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Solution Overview
Problem
Existing image inpainting methods using convolution neural networks often produce poor results with visible artifacts when removing unwanted content from images, as they fail to sufficiently sample relevant contexts and instead sample many irrelevant ones, especially in indoor images with occlusions and complex structures.
Innovation Solution
The proposed system employs multimodal structural guidance (MSG) that uses a semantic mask and surface normal map to identify and rank neighboring contexts, and an attention mechanism to sample relevant pixel information, avoiding false-relevant contexts and dynamically adjusting the receptive field to ensure sufficient context sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If convolution neural networks are used for image inpainting, then the inpainting process can be automated, but visible artifacts are generated and reconstruction quality deteriorates
Solution Approach 1:
The patent segments the inpainting process into multiple specialized modules: a semantic mask generation module that identifies object boundaries, a surface normal map module that preserves geometric continuity, and a context sampling module that selectively gathers relevant information. This segmentation allows each module to specialize in a specific aspect of reconstruction, avoiding the artifacts produced by monolithic convolutional approaches while maintaining full automation.
Solution Approach 2:
The patent introduces intermediary structures between the automated inpainting process and the final reconstruction: semantic masks act as intermediaries to guide content selection, surface normal maps serve as intermediaries to preserve geometric consistency, and an attention mechanism acts as an intermediary to selectively weight different context sources. These intermediaries enable automated processing while preventing artifact generation.
2Ease of manufacture
If convolution neural networks sample contexts for inpainting, then the process is simple to implement, but irrelevant contexts are sampled and precision is lost
Solution Approach 1:
The patent replaces static convolutional sampling with dynamic context sampling driven by an attention mechanism. The attention mechanism dynamically adjusts which contexts are sampled based on their relevance to the target region, using semantic masks and surface normal maps to guide selection. This dynamic approach maintains implementation feasibility while dramatically improving context sampling precision by adapting to the specific characteristics of each inpainting task.
Solution Approach 2:
The patent changes the sampling parameters from fixed convolutional kernels to variable attention weights that adapt based on semantic and geometric information. By modifying how contexts are weighted and selected—using semantic masks to identify relevant regions and surface normal maps to preserve geometric continuity—the system achieves precise context sampling without sacrificing implementation simplicity, as the changes are integrated into the existing neural network framework.
3Productivity
If standard inpainting methods are used, then processing speed is maintained, but artifact generation increases and image quality suffers
Solution Approach 1:
The patent performs preliminary actions before the main inpainting reconstruction: generating semantic masks to identify object boundaries and surface normal maps to capture geometric information. These preliminary structures are created once and then used to guide the entire reconstruction process, enabling the system to maintain processing speed while avoiding artifact generation by having the guidance information ready in advance rather than computing it iteratively during reconstruction.
Data Source
AI summary
An inpainting method includes obtaining image information at an electronic device, where the image information identifies an area corresponding to a removed object within an image. The method also includes reconstructing the area corresponding to the removed object by (i) applying a semantic mask and a surface normal map to identify and rank neighboring contexts of the area and (ii) sampling, using an attention mechanism, the ranked contexts to generate pixel information for the area. The method further includes rendering the image with the reconstructed area.