3D Map Pixel Alignment via Confidence Value Rendering
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Solution Overview
Problem
Current technologies face challenges in accurately processing and registering media content for rendering in 3D maps, leading to misalignment and visual artifacts due to inaccuracies in 3D geometry and lack of depth information, especially when integrating user-captured images or videos with 3D models.
Innovation Solution
A method and system that determine confidence values for pixel alignment by processing image pixels and metadata to estimate geometric distortion, allowing for accurate rendering of media content on 3D maps by projecting pixels based on confidence values, and dynamically adjusting image visibility based on camera angles and distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D reconstruction techniques are used to register media contents with real-world locations, then the accuracy of media content integration is improved, but visual artifacts and misalignment occur due to inaccuracies in 3D geometry and lack of depth information
Solution Approach 1:
The patent replaces traditional geometric projection methods with a confidence value-based selection mechanism. Instead of relying solely on 3D geometry calculations, the system evaluates multiple candidate pixels and selects the best match based on computed confidence values that account for geometric distortion, thereby eliminating visual artifacts caused by inaccurate projection.
Solution Approach 2:
The patent introduces confidence values as an intermediary metric between the 3D map pixels and media content pixels. This intermediary allows the system to evaluate the quality of potential pixel mappings and select only those with high confidence, thereby preventing misalignment and visual artifacts while maintaining accurate registration.
2Area of stationary object
If multiple images are associated with the same part of the 3D model, then more comprehensive coverage is achieved, but view selection complexity increases and rendering accuracy decreases
Solution Approach 1:
The patent segments the image selection process into discrete candidate evaluations. Each candidate pixel from multiple images is independently assessed using confidence value calculations, allowing the system to selectively incorporate the best matching pixels from different images while maintaining rendering precision and avoiding the complexity of holistic view selection.
3Adaptability or versatility
If user-captured images are integrated into 3D maps, then user content contribution is enabled, but alignment accuracy deteriorates due to lack of depth information and geometric distortion
Solution Approach 1:
The patent changes the evaluation parameter from simple geometric projection to confidence value calculation that incorporates geometric distortion assessment. This allows user-captured images with varying quality and perspective to be integrated into the 3D map while maintaining alignment accuracy through selective rendering based on confidence thresholds.
Data Source
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AI summary
An approach is provided for accurate processing and registering of media content for rendering in 3D maps and other applications. The approach includes determining at least one first pixel of at least one image that geometrically corresponds to at least one second pixel of at least one rendered three-dimensional map. Further, the approach includes processing and/or facilitating a processing of (a) the at least one first pixel; (b) the at least one second pixel; (c) metadata associated with at least one of the at least one first pixel and the second pixel; or (d) a combination thereof to determine at least one confidence value, wherein the at least one confidence value is indicative of an estimated level of geometric distortion resulting from projecting the at least one first pixel onto the at least one second pixel. Furthermore, the approach includes determining whether to cause, at least in part, a rendering of the at least one first pixel onto the at least one rendered three-dimensional map based, at least in part, on the confidence value.