Panoptic Segmentation Mask for Overlapping Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current panoptic segmentation techniques struggle to predict objects behind transparent or occluding objects, as they require every pixel to exclusively belong to a single object, limiting their ability to handle overlapping instances.
Innovation Solution
The proposed solution involves generating a panoptic segmentation mask by combining semantic and instance masks, where the instance mask includes an indication of whether an instance of a countable object is hidden behind another object, allowing for the prediction of overlapping objects without the need for explicit fusion of masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current panoptic segmentation techniques are used to ensure every pixel belongs to a single object, then segmentation simplicity is maintained, but the ability to predict objects behind transparent or occluding objects is lost
Solution Approach 1:
The patent divides the segmentation task into two separate neural networks: a semantic segmentation network that handles amorphous regions and a instance segmentation network that handles countable objects. This segmentation of the segmentation task allows each network to specialize in specific object types, enabling the system to predict objects behind transparent or occluding objects while maintaining manageable complexity through division of labor
2Measurement precision
If semantic segmentation and instance segmentation are performed separately, then each segmentation task can be optimized independently, but the computational resources required increase
Solution Approach 1:
The patent combines the outputs of the semantic segmentation network and instance segmentation network into a unified panoptic segmentation mask. By merging the specialized results of both networks, the system achieves high segmentation accuracy for both amorphous regions and countable objects while avoiding the need for a single complex network that would require significantly more computational resources
3Device complexity
If a single neural network is used for panoptic segmentation, then device complexity is reduced, but the ability to handle overlapping instances and transparent objects is limited
Solution Approach 1:
The patent creates a multi-functional system where the semantic segmentation network handles amorphous regions and the instance segmentation network handles countable objects, including transparent and occluding objects. Both networks work together to produce a comprehensive panoptic segmentation mask, allowing the system to adapt to various object types and overlapping instances while keeping individual network complexities manageable
Data Source
AI summary
Systems and methods for generating a panoptic segmentation mask for an input image. The methods include receiving the input image comprising a plurality of pixels, generating a semantic mask and an instance mask from the input image, and combining the semantic mask and the instance mask to generate a panoptic mask for the input image. The semantic mask includes a single-channel mask that associates each pixel in the input image with a corresponding one of a plurality of labels. The instance mask includes a plurality of masks, where each of the plurality of masks identifies an instance of a countable object in the input image, and is associated with an indication of whether that instance of the countable object is hidden behind another object in the input image.