3D Mesh Boundary Refinement Using Semantic Segmentation
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Solution Overview
Problem
Existing 3D mesh generation techniques struggle to accurately identify and measure objects that are coplanar, such as doors and walls, due to limited storage capacity and reliance on geometry information alone, leading to inaccuracies in representing physical dimensions.
Innovation Solution
The integration of semantic segmentation techniques with depth data to classify pixels into recognized objects, allowing for the refinement of 3D mesh representations by aligning semantic segmentation images with geometry information, thereby improving the accuracy of object boundaries and measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only geometry information from depth data is used for 3D mesh generation, then device complexity is reduced, but measurement precision deteriorates due to inability to distinguish coplanar objects
Solution Approach 1:
The patent combines geometry information from depth data with semantic segmentation information from color images to create a unified 3D mesh representation. This merging allows the system to leverage both geometric accuracy and semantic object identification, resolving the contradiction by integrating multiple information sources rather than relying on depth data alone.
Solution Approach 2:
The patent introduces semantic segmentation as an intermediary process that bridges color image data and depth data. The semantic segmentation results act as a mediator to guide the refinement of 3D mesh boundaries, enabling accurate distinction of coplanar objects without requiring complex direct analysis of raw depth data.
2Measurement precision
If semantic segmentation is integrated with depth data, then measurement precision improves for coplanar objects, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent applies segmentation by dividing the processing into distinct stages: initial 3D mesh generation from depth data, semantic segmentation of color images, and subsequent refinement of mesh boundaries using segmentation results. This segmented approach manages complexity by breaking down the complex task into manageable, sequential processing steps.
Solution Approach 2:
The patent applies local quality by using semantic segmentation to identify and process specific regions of interest (coplanar objects) differently from the rest of the scene. The refinement process focuses computational resources on areas where coplanar objects exist, improving measurement precision locally without uniformly increasing complexity across the entire processing pipeline.
3Measurement precision
If multiple data sources (color images and depth data) are integrated, then measurement precision improves, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent applies preliminary action by performing semantic segmentation on color images before using the results to refine the 3D mesh. The semantic segmentation is computed in advance to identify object boundaries and relationships, which then guide the subsequent mesh refinement process. This preliminary classification reduces the complexity of the refinement step and enables more efficient processing.
Solution Approach 2:
The patent maintains continuity of useful action by processing color images and depth data in an integrated workflow where each processing step builds on the previous results. The semantic segmentation results are immediately utilized for mesh refinement without intermediate storage or reprocessing, ensuring continuous utilization of extracted information and minimizing idle time between processing stages.
Data Source
AI summary
A mesh model of a 3D space is modified based on semantic segmentation data to more accurately represent boundaries of an object in the 3D space. In one aspect, semantic segmentation images define one or more boundaries of the object. The semantic segmentation images are projected to a 3D mesh representation of the 3D space, and the 3D mesh representation is updated based on the one or more boundaries in the projected semantic segmentation image. In another aspect, the 3D mesh representation is updated based on one or more boundaries defined by the semantic segmentation images as applied to a point cloud of the 3D space.