Image Generation Viewpoint Constraint via Scene Density
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Solution Overview
Problem
Existing image generation technologies do not impose constraints on the rendering viewpoint, leading to poor image quality with missing or omitted objects in the target scene space.
Innovation Solution
An image generation method that involves obtaining density information from a target model of a target scene space, determining a first constraint condition based on this density information, selecting a target viewpoint that avoids fog and object information, and rendering a viewpoint image using the target model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If no constraints are imposed on the rendering viewpoint, then the rendering process is simple and flexible, but the image quality deteriorates with missing or omitted objects
Solution Approach 1:
The patent applies preliminary action by pre-calculating density information for all points in the target scene space before rendering. This density information is stored and then used to determine constraint conditions that guide viewpoint selection. By performing this preparation in advance, the system ensures high image quality without adding complexity to the actual rendering process, as the constraints are already established before rendering begins.
Solution Approach 2:
The system uses the density information generated by the target model itself to automatically determine constraint conditions for viewpoint selection. The target model serves both to generate the scene representation and to provide the density data that constrains its own rendering process. This self-service approach improves image quality through automated constraint application without requiring external intervention or complex additional systems.
2Reliability
If the rendering viewpoint is determined without constraints, then the operation is simple, but the reliability of representing all objects deteriorates
Solution Approach 1:
The patent implements feedback by using density information from the target scene space to continuously guide viewpoint selection. The system calculates density values for different viewpoints, uses this feedback to determine constraint conditions, and selects viewpoints that satisfy these constraints. This closed-loop feedback mechanism ensures reliable representation of all objects while automating the viewpoint selection process, maintaining ease of operation without sacrificing completeness.
3Measurement precision
If density information is used to determine constraint conditions, then viewpoint selection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by calculating density information selectively for different regions of the target scene space rather than uniformly across all points. The constraint conditions are determined based on local density characteristics at specific viewpoints, allowing accurate viewpoint selection while reducing unnecessary computations in regions that do not affect the final rendering quality. This localized approach improves accuracy while managing computational energy consumption.
Data Source
AI summary
The present disclosure discloses an image generation method. The method includes: obtaining density information by a target model corresponding to a target scene space; determining a first constraint condition based on the density information; determining a target viewpoint from the target scene space based on the first constraint condition; and rendering the viewpoint image corresponding to the target viewpoint by the target model. Where the target model is trained to output a viewpoint image corresponding to any viewpoint after inputting the any viewpoint in the target scene space, the density information is a rendering parameter required for image rendering by the target model, and the density information represents transparency of a point in the target scene space.