2D to 3D Image Conversion Pre-processing for Occlusion Handling
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Solution Overview
Problem
Current 2D to 3D conversion processes face inefficiencies in handling occlusions and transparencies, with existing methods being computationally expensive and requiring significant operator input, as they often necessitate reconstructing occluded areas after the conversion process is complete.
Innovation Solution
The proposed method involves segmenting discrete elements in 2D images, estimating and reconstructing occluded areas before conversion, and re-compositing them to approximate the original image sequence, allowing for accurate and efficient handling of occlusions during the 2D to 3D conversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If occluded areas are reconstructed after 2D to 3D conversion, then the conversion process can be completed, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by estimating and reconstructing occluded areas before the 2D to 3D conversion process. The system predicts which areas will be occluded during conversion and pre-reconstructs them using contextual information from surrounding pixels and depth maps, eliminating the need for expensive post-conversion inpainting operations.
Solution Approach 2:
The patent segments the image into discrete elements and processes them individually. By dividing the image into multiple segments and handling each one separately during the conversion process, the system can efficiently manage occluded areas without requiring comprehensive post-conversion processing of the entire image.
2Manufacturing precision
If manual adjustment of image data is performed after conversion, then accuracy can be improved, but the process becomes time-consuming and requires repetitious practice
Solution Approach 1:
The patent implements self-service by using automated algorithms that automatically reconstruct occluded areas based on contextual information from the image itself. The system leverages depth maps, surrounding pixel data, and algorithmic prediction to autonomously fill in occluded regions without requiring manual operator intervention or repetitious practice.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously refines its reconstruction based on the original image data and depth information. By comparing the reconstructed areas with the original context and adjusting accordingly, the system achieves high accuracy without manual adjustment.
3Manufacturing precision
If replacement image data is generated to match the alternate perspective, then the visual quality improves, but the complexity of matching and complementing increases
Solution Approach 1:
The patent changes parameters by using depth information and contextual data to guide the reconstruction process. By adjusting reconstruction parameters based on depth maps and surrounding pixel characteristics, the system generates visually accurate replacement data while simplifying the matching process through parameter-driven automation.
Solution Approach 2:
The patent introduces an intermediary approach by using depth maps and contextual information as mediators between the original image data and the reconstructed occluded areas. These intermediaries simplify the complex matching process by providing structured guidance on how to reconstruct different regions, reducing the overall complexity of generating coherent visual data.
Data Source
AI summary
Disclosed herein are methods and systems of efficiently, effectively, and accurately preparing images for a 2D to 3D conversion process by pre-treating occlusions and transparencies in original 2D images. A single 2D image, or a sequence of images, is ingested, segmented into discrete elements, and the discrete elements are individually reconstructed. The reconstructed elements are then re-composited and ingested into a 2D to 3D conversion process.