Messaging 3D Data Generation Using Segmentation and Depth Maps
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Solution Overview
Problem
The challenge of enhancing user experiences with digital images through image processing operations in changing conditions, such as scale, noise, lighting, and geometric distortion, is computationally intensive and complex, particularly in messaging systems where interactive 3D media is desired.
Innovation Solution
A messaging system infrastructure that supports the creation and sharing of 3D messages, incorporating depth-based media, motion sensor input, and augmented reality content generators to render spatial details, allowing for interactive 3D images that change perspective with viewer movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image processing operations are performed to enhance user experiences with digital images, then user experience quality is improved, but computational intensity and system complexity increase
Solution Approach 1:
The system segments image processing into distinct modules: depth map generation, segmentation mask creation, background inpainting, and 3D effect rendering. Each module processes specific aspects of the image independently, reducing overall computational complexity while maintaining enhanced user experience through specialized processing functions.
Solution Approach 2:
The system performs preliminary processing by generating depth maps and segmentation masks before applying 3D effects. These pre-computed structures enable subsequent rendering operations to focus only on specific regions and apply effects efficiently, reducing real-time computational requirements while preserving enhanced user experience.
2Adaptability or versatility
If 3D effects and augmented reality content are applied to images, then interactivity and spatial detail are improved, but processing time and computational resources increase
Solution Approach 1:
The system applies 3D effects and augmented reality content locally to specific regions identified by segmentation masks rather than processing the entire image uniformly. This targeted approach maintains high interactivity and spatial detail in relevant areas while reducing processing time by skipping unnecessary computations in other regions.
Solution Approach 2:
The system dynamically adjusts the level of processing and effect application based on image content and user interaction. Processing intensity and effect complexity are modified in real-time to balance interactivity requirements with available computational resources, reducing processing time when maximum interactivity is not required.
3Manufacturing precision
If depth-based media and motion sensor input are incorporated, then spatial detail and realism are improved, but device requirements and system complexity increase
Solution Approach 1:
The system uses intermediary processing structures such as depth maps and segmentation masks as mediators between raw sensor input and final 3D rendering. These intermediaries simplify the relationship between device requirements and output quality, allowing spatial detail to be achieved through algorithmic processing rather than requiring complex hardware for every processing stage.
Data Source
AI summary
The subject technology applies a three-dimensional (3D) effect to image data and depth data based at least in part on an augmented reality content generator. The subject technology generates a segmentation mask based at least on the image data. The subject technology performs background inpainting and blurring of the image data using at least the segmentation mask to generate background inpainted image data. The subject technology generates a packed depth map based at least in part on the a depth map of the depth data. The subject technology generates, using the processor, a message including information related to the applied 3D effect, the image data, and the depth data.