Image Reconstruction via Panoptic Segmentation and Recurring Inpainting
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Solution Overview
Problem
Existing aerial imaging techniques, such as structure from motion, are inefficient and time-consuming due to the need to capture images from multiple angles to map occluded areas, especially when multiple objects occlude the same area from different angles.
Innovation Solution
The method involves panoptic segmentation of the image, which combines semantic segmentation to classify pixels into classes and instance segmentation to identify specific objects, followed by recurring image inpainting. This process iteratively applies masks to the image based on panoptically segmented objects and fills in the masked areas with plausible content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If structure from motion is used to map occluded areas, then complete coverage of invisible areas can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent applies segmentation by dividing the occluded area into multiple invisible portions, each associated with a different occluding object. By performing instance segmentation to identify individual objects and their corresponding invisible areas, the system can process and reconstruct each occluded region independently and efficiently, rather than requiring complete multi-angle imaging of the entire scene
Solution Approach 2:
The patent performs preliminary action by using semantic segmentation and instance segmentation to pre-identify occluding objects and their associated invisible areas before reconstruction. This preliminary classification allows the system to target specific occluded regions for reconstruction, eliminating the need for time-consuming multi-angle image capture while maintaining complete coverage
2Reliability
If multiple images are captured from different angles to map occluded areas, then all invisible areas can be covered, but the complexity of the imaging process increases
Solution Approach 1:
The patent replaces the mechanical system of multi-angle image capture with computational methods. Instead of physically moving the imaging device to multiple positions, the system uses semantic segmentation and instance segmentation algorithms to identify occluded areas and generates reconstructions computationally, thereby eliminating the need for complex multi-angle imaging hardware and operations
3Manufacturing precision
If recurring image inpainting is performed for each segmented object, then reconstruction accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the reconstruction task into independent instance-level operations. By performing instance segmentation to identify individual occluding objects and their associated invisible areas, the system can apply recurring image inpainting to each segmented region independently. This allows parallel processing of multiple occluded areas, improving overall efficiency while maintaining high reconstruction accuracy for each object
Solution Approach 2:
The patent applies partial action by performing recurring image inpainting selectively on only the invisible portions associated with each segmented object, rather than processing the entire image. This targeted approach concentrates computational resources on the specific occluded areas that require reconstruction, improving accuracy where needed while minimizing unnecessary processing time
Data Source
AI summary
A method of reconstructing an image. The method includes performing panoptic segmentation of the image and performing instance segmentation of the image. The method further includes performing recurring image inpainting of the image. The recurring image inpainting of the image includes applying a first mask corresponding to the first object to the image, inpainting the first mask to form a partially reconstructed image, applying a second mask corresponding to the second object to the partially reconstructed image, and inpainting the second mask.