3D Rendering with Angular Compensation Neural Network

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Solution Overview

Problem

Current techniques for converting two-dimensional (2D) video content to three-dimensional (3D) content are labor-intensive and result in sub-optimal quality, limiting the immersive experience for viewers, especially in glasses-free 3D displays due to limitations in optics and the complexity of manual depth map creation.

Innovation Solution

A neural network-based system that processes 2D video content to generate 3D images by analyzing motion vectors, image characteristics, and texture, using adaptive weights and learning techniques to create high-quality depth maps, capable of real-time conversion and optimization for various 3D display technologies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If manual depth map creation techniques are used for 2D to 3D conversion, then some level of 3D content can be produced, but the process becomes labor-intensive and time-consuming

Engineering Contradiction:

Improveautomation of 2D to 3D conversionVSAvoidtime required for conversion

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system uses the 2D video content itself to generate the depth map through automated analysis of motion vectors, image characteristics, and texture information, eliminating the need for manual depth map creation and achieving self-service 2D to 3D conversion

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes with an automated computational system that analyzes video content and generates depth maps using algorithms that process motion vectors, image characteristics, and texture data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If conventional 2D to 3D conversion techniques are used, then 3D content can be generated, but the quality is sub-optimal and the process is complicated

Engineering Contradiction:

Improvequality of 3D contentVSAvoidcomplexity of conversion process

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The conversion process is divided into distinct analytical components: motion vector analysis, image characteristic evaluation, and texture assessment, each contributing specific information to the overall depth map generation for high-quality 3D content

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system transitions from 2D video content to 3D content by introducing a depth dimension through automated depth map generation, creating accurate three-dimensional representations from two-dimensional source material

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If manual techniques are used for 2D to 3D conversion, then some 3D content is produced, but the amount of desirable content remains limited

Engineering Contradiction:

Improveamount of 3D content producedVSAvoidlevel of manual labor required

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The automated system processes 2D video content independently, generating high-quality 3D content without manual intervention, thereby dramatically increasing productivity and the volume of desirable 3D content that can be produced

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10225542B23D system including rendering with angular compensation

Publication Date: 2019.03.05 VEFXI CORPORATION

AI summary

A three dimensional system including rendering with angular compensation.