Adaptive View-Depth Discontinuity for 3D Scene Crop
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Solution Overview
Problem
Existing image processing techniques fail to effectively and efficiently remove distant background objects from oblique and façade views in 3D image reconstruction, requiring manual intervention or relying on camera position-based cropping that over-crops points not directly viewed by the camera.
Innovation Solution
The method utilizes depth maps from multiple views to determine a cutoff depth, marking points beyond this as low confidence, and aggregates confidence scores to discard distant objects while keeping high-confidence subject elements intact, using adaptive view penalties and cutoff relaxation to differentiate between subject and background based on view angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If camera position-based cropping is used, then Nadir shots are handled correctly, but oblique and façade shots result in over-cropping
Solution Approach 1:
The patent applies dynamics by making the cropping mechanism adaptive to different view angles. Instead of a static camera position-based approach, the system dynamically adjusts cropping parameters based on the specific view angle (nadir, oblique, façade), allowing each view type to have optimized cropping behavior that prevents over-cropping while maintaining accuracy.
Solution Approach 2:
The patent implements local quality by applying different cropping strategies to different parts of the image based on view angle. Each view angle (nadir, oblique, façade) receives a tailored cropping approach, with confidence thresholds and depth-based filtering adjusted locally to match the characteristics of that specific view, rather than applying a uniform cropping method globally.
2Manufacturing precision
If manual cleanup is used, then background removal precision is high, but processing time increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically perform background removal without requiring manual intervention. The confidence-based filtering and depth-map analysis algorithms autonomously identify and remove background elements, replacing the need for manual cleanup while maintaining high precision through automated confidence thresholding.
Solution Approach 2:
The patent substitutes the mechanical manual cleanup process with an automated computational system. Instead of relying on human operators to manually remove background elements, the system uses depth maps, confidence scores, and view-angle-specific algorithms to automatically filter and remove background objects, significantly reducing processing time while maintaining precision.
3Productivity
If depth-based filtering is applied, then background objects are removed, but subject elements may be incorrectly discarded
Solution Approach 1:
The patent applies local quality by differentiating between subject and background elements through view-angle-specific confidence thresholds. Instead of applying a uniform depth-based filter, the system adjusts filtering parameters locally based on the view angle and confidence scores, ensuring that subject elements are preserved while background objects are removed. This localized approach prevents incorrect discarding of subject elements.
Solution Approach 2:
The patent implements feedback through confidence score calculation and aggregation across multiple views. The system continuously evaluates confidence scores for each point cloud element, using feedback from depth map analysis and view-angle-specific metrics to adjust filtering decisions. This feedback mechanism ensures that subject elements with high confidence scores are retained while low-confidence background elements are removed, maintaining reliability.
Data Source
AI summary
A method, apparatus, and system provide the ability to crop a three-dimensional (3D) scene. The 3D scene is acquired and includes multiple 3D images (with each image from a view angle of an image capture device) and a depth map for each image. The depth values in each depth map are sorted. Multiple initial cutoff depths are determined for the scene based on the view angles of the images (in the scene). A cutoff relaxation depth is determined based on a jump between depth values. A confidence map is generated for each depth map and indicates whether each depth value is above or below the cutoff relaxation depth. The confidence maps are aggregated into an aggregated model. A bounding volume is generated out of the aggregated model. Points are cropped from the scene based on the bounding volume.