Occlusion-Aware Texture Estimation for 3D Space Scanning
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Solution Overview
Problem
Existing methods for generating 3D models of real-world spaces struggle with accurately capturing texture data when objects occlude interior surfaces during 3D scanning, leading to noisy sampling and incomplete texture representation.
Innovation Solution
The system employs object detection using machine learning and inpainting techniques to identify occluding objects, estimate texture data for occluded areas, and fill in missing texture information based on surrounding patterns, ensuring accurate texture representation of interior surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If 3D scanning is performed in real-world spaces with occluding objects, then complete coverage of interior surfaces is achieved, but texture data quality deteriorates due to noisy sampling from occluded areas
Solution Approach 1:
The system segments the texture sampling process by identifying and separating occluding objects from interior surfaces using object detection algorithms. By dividing the scene into occluder and occluded regions, the system can selectively process texture data from non-occluded areas, preventing noisy sampling from contaminating the overall texture map.
Solution Approach 2:
The system performs preliminary object detection and occlusion analysis before final texture mapping. By identifying occluding objects in advance and calculating their occlusion zones, the system can pre-filter texture sampling regions to exclude areas that would contribute noisy data, thereby improving overall texture accuracy before the mapping process begins.
2Productivity
If traditional texture mapping is used without occlusion handling, then processing speed is maintained, but texture completeness deteriorates due to missing data from occluded surfaces
Solution Approach 1:
The system introduces an intermediary occlusion map that acts as a mediator between the raw scan data and the final texture map. This occlusion map, generated through object detection, guides the texture mapping process by indicating which regions should be sampled and which should be excluded, thereby recovering complete texture information without significantly impacting processing speed.
3Measurement precision
If occlusion detection and inpainting are added to the pipeline, then texture accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs a unified object detection framework that simultaneously serves multiple functions: identifying occluding objects, calculating occlusion zones, and guiding texture sampling. This multi-functional approach consolidates what could be separate complex modules into a single versatile processing pipeline, improving texture accuracy while limiting the increase in overall system complexity.
Data Source
AI summary
A computer-implemented is disclosed. The method includes: obtaining three-dimensional geometric scan data and first texture data for a defined space, the defined space defined by interior surfaces including at least one wall; detecting an object occluding a first one of the interior surfaces of the defined space based on the three-dimensional geometric scan data; in response to detecting the object: identifying a corresponding portion of the first interior surface occluded by the detected object; and estimating texture data for the corresponding portion of the first interior surface based on the first texture data for the defined space, and outputting texture data for the interior surfaces based on the first texture data for the defined space and the estimated texture data for the corresponding portion of the first interior surface