Vector Graphic Parsing for 3D Object and Motion Transformation
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Solution Overview
Problem
Existing technologies struggle to efficiently parse and transform 2D vector graphics into 3D renderable objects and associated movement patterns for various contexts, such as sports plays or diagrams, due to the complexity and variability of SVG file formats.
Innovation Solution
A parsing and transformation engine that identifies renderable objects and movement patterns in SVG files by using predefined class definitions and rules, converting them into a simplified format like WPF, and then transforming the data for submission to a 3D graphics engine or 2D formats like Visio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a parsing engine uses predefined class definitions and rules to identify renderable objects in SVG files, then the parsing accuracy and object identification reliability improve, but the device complexity and difficulty of adapting to different contexts increase
Solution Approach 1:
The parsing engine employs dynamic rule sets that can be adjusted and modified based on different SVG file types and contexts. The engine transitions from static predefined classes to dynamic adaptive parsing rules that learn from and adapt to various diagram formats, maintaining reliability while managing complexity through controlled flexibility.
Solution Approach 2:
The system changes parsing parameters and rules based on the specific context of each SVG file. By modifying classification thresholds, object properties, and interpretation rules according to the diagram type (sports plays, medical imaging, technical drawings), the engine maintains high identification reliability across diverse applications without requiring complete redesign for each context.
2Loss of information
If the parsing engine processes complex SVG files with multiple shapes, lines, and movement patterns, then the completeness of extracted information improves, but the processing time and productivity decrease
Solution Approach 1:
The parsing engine divides complex SVG files into manageable segments by identifying and processing distinct object types separately - shapes, lines, text elements, and movement patterns are parsed independently through specialized sub-routines. This segmentation allows parallel processing and optimizes the extraction of each element type without requiring complete analysis of the entire file structure at once.
Solution Approach 2:
The engine performs preliminary actions by pre-defining class structures, object templates, and movement pattern categories before processing the SVG file. By establishing the framework of expected objects and their properties in advance, the parsing process becomes more efficient as it can quickly match SVG elements to predefined categories rather than building structures from scratch during processing.
3Adaptability or versatility
If the system transforms parsed data into multiple output formats (3D graphics, 2D formats, database storage), then the versatility and adaptability improve, but the device complexity and transformation overhead increase
Solution Approach 1:
The parsing engine creates a universal intermediate data structure that can be transformed into multiple output formats (3D graphics, 2D formats, database storage) without requiring separate parsing processes for each format. This multi-functional approach allows the same core parsing logic to serve diverse applications, reducing overall system complexity while maintaining versatility through a single standardized interpretation layer.
Solution Approach 2:
The system introduces an intermediary standardized data structure that acts as a mediator between the SVG input and various output formats. This intermediate representation layer simplifies transformations by providing a common language that can be converted to 3D graphics, 2D formats, or database storage through relatively simple translation processes rather than complex direct transformations.
4Measurement precision
If the parsing engine applies context-specific rules for different diagram types (sports plays, medical imaging, technical drawings), then the measurement precision and context accuracy improve, but the difficulty of detecting and measuring increases
Solution Approach 1:
The engine performs preliminary classification to identify the diagram context (sports plays, medical imaging, technical drawings) before applying detailed parsing rules. By detecting the general category first through preliminary analysis of file metadata, structure patterns, and element distributions, the system prepares the appropriate context-specific rule set in advance, making subsequent precise measurements and interpretations more straightforward.
Solution Approach 2:
The parsing engine applies different levels of precision and interpretation rules locally based on the detected context. For example, sports plays receive specific attention to player positioning and movement patterns, while medical imaging receives focused analysis on anatomical structures and relationships. This localized quality adjustment optimizes measurement precision for each context without requiring the engine to maintain maximum precision for all possible diagram types simultaneously.
Data Source
AI summary
A system and method are presented that scans a vector graphics file for image elements that represent individual objects and for image elements that represent movement or actions for those objects. Objects, each possibly represented by multiple shapes, arrows, lines, or shading, are identified as parsed into a standard class definition. In one embodiment, the class definitions and the rule set applied to perform this parsing are pre-defined according to the content of the SVG file. The class definition can then be used to create input into an API for a 3D graphics engine to render the objects with their associated movements.