Adjustable Depth Layers for 3D Image Temporal Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing three-dimensional image generation technologies face challenges in maintaining temporally consistent and stable depth maps, particularly when dealing with static and dynamic elements, as well as changes in brightness, illumination, and motion, leading to potential flickering or fluctuation in depth perception.
Innovation Solution
The system employs depth layers and depth layer volumes to dynamically manage depth values, allowing for adaptive redistribution of depth values between layers, ensuring that central depth locations remain consistent, and introduces concepts like depth layer thickness adjustment and dynamic allocation to maintain temporal stability and accuracy in depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional depth map generation methods are used, then the processing is simpler, but temporal consistency and stability of depth maps deteriorate, causing flickering and fluctuation in depth perception
Solution Approach 1:
The depth map is segmented into multiple depth layers, each representing different depth ranges. This segmentation allows independent management and adjustment of each layer's depth values, improving temporal consistency by preventing flickering between frames while maintaining system tractability through modular layer handling.
Solution Approach 2:
The system dynamically adjusts depth layer thickness and redistributes depth values across layers based on temporal consistency requirements. Depth layer parameters such as thickness and value distribution are adaptively modified between frames to eliminate flickering, transforming the static depth map into a dynamically stable multi-layer structure.
2Adaptability or versatility
If depth layers with fixed thickness are used, then the system is simpler to manage, but the ability to adapt to different depth ranges and objects is reduced
Solution Approach 1:
Depth layer thickness is made dynamic rather than fixed. The system automatically adjusts the thickness of each depth layer based on the distribution of objects and depth values in the scene, enabling adaptation to various depth ranges and object configurations while managing complexity through automated adjustment algorithms.
Solution Approach 2:
The system changes key parameters of depth layers including thickness, depth value ranges, and value distribution across layers. These parameter adjustments enable the system to adapt to different scenes and objects dynamically, transforming fixed-depth-layer systems into flexible, scene-adaptive structures.
3Measurement precision
If depth values are uniformly distributed across all layers, then the system is easier to implement, but the precision of depth representation for specific objects is reduced
Solution Approach 1:
Instead of uniform depth value distribution, the system applies local quality by distributing depth values differently across various depth layers based on object importance and scene requirements. Critical depth regions receive more precise value allocation, improving measurement precision while managing complexity through localized optimization strategies.
Solution Approach 2:
The depth value distribution parameter is changed from uniform to non-uniform across layers. The system dynamically adjusts how depth values are allocated to different layers based on scene content, enhancing depth representation precision for important objects while maintaining implementability through automated distribution algorithms.
Data Source
AI summary
A system and method for generating a three-dimensional image using depth layers is provided. A plurality of depth layers may be generated from the image, where each depth layer has a height, a width, and a thickness. Certain regions of the image may be assigned to one of the plurality of depth layers. A depth map may be generated based on the depth layers. Further, a disparity map may be generated based on both the depth map and the depth layers. A stereo view of the image may then be rendered based on the disparity map.