Adaptive 3D Space Texturing Using Structural Classification
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Solution Overview
Problem
Existing methods for generating three-dimensional modeling from captured images in a space fail to efficiently perform texturing, leading to unnatural or distorted displays, especially in complex spaces when using images from a single capture point.
Innovation Solution
An adaptive three-dimensional space generation method that classifies spaces into types based on structural characteristics and selects appropriate images for texturing, using multiple images for complex spaces to ensure natural and accurate representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If texturing is performed using image information from a single captured image, then the texturing process is simple and fast, but the texturing becomes unnatural or distorted in complex spaces
Solution Approach 1:
The patent segments the space into simple-type and complex-type categories based on structural characteristics. For simple spaces, single-image texturing is used, while for complex spaces, multiple images are selected and integrated. This segmentation allows the system to adapt the texturing approach to the specific space complexity, maintaining efficiency for simple spaces while achieving accuracy for complex spaces.
Solution Approach 2:
The patent implements a dynamic texturing system that automatically adjusts the number of images used for texturing based on the detected space complexity. The system evaluates structural characteristics and dynamically selects between single-image and multi-image texturing approaches, optimizing both efficiency and accuracy according to the specific space being modeled.
2Manufacturing precision
If multiple images are used for texturing complex spaces, then the texturing accuracy and naturalness improve, but the processing complexity and time increase
Solution Approach 1:
The patent divides the texturing process into two distinct pathways based on space type: simple-space texturing using single images and complex-space texturing using multiple images. This segmentation reduces overall system complexity by allowing different processing pipelines for different space types, rather than requiring a complex multi-image processing system for all cases.
Solution Approach 2:
The patent applies different texturing qualities and processing methods to different parts of the overall system based on space complexity. Simple spaces receive streamlined single-image processing, while complex spaces receive enhanced multi-image processing. This local quality approach optimizes resource allocation and reduces unnecessary complexity in simple cases.
3Manufacturing precision
If multiple images are used for texturing, then the realism and accuracy of space modeling improve, but the processing time and computational resources increase
Solution Approach 1:
The patent segments processing time allocation based on space complexity evaluation. Simple spaces are processed quickly using single images, while complex spaces receive extended processing time with multiple images. This segmentation ensures that time resources are allocated efficiently, spending more time only where necessary for complex spaces.
Solution Approach 2:
The patent implements dynamic processing that adjusts computational resources and processing time based on detected space complexity. The system automatically scales the processing intensity and time investment according to the specific requirements of each space, avoiding unnecessary processing time for simple spaces while ensuring adequate time for complex spaces.
Data Source
AI summary
An adaptive three-dimensional space generation system and a method therefor are provided. The adaptive three-dimensional space generation method comprises allowing an adaptive three-dimensional space generation system to determine whether a space is a first-type space or a second-type space depending on structural characteristics of the space based on a plurality of images captured from different locations in the space, allowing the adaptive three-dimensional space generation system to adaptively select an image for texturing the space among the images depending on whether the space is the first-type space or the second-type space, and performing texturing of the space based on the image selected by the adaptive three-dimensional space generation system.