Arbitrary View Generation Using Depth Refinement and Image Merging
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Solution Overview
Problem
Existing rendering techniques face a trade-off between quality and speed, with high-quality rendering requiring significant processing resources and time, while lower quality but faster rendering techniques are inadequate for interactive, real-time applications.
Innovation Solution
A system that generates arbitrary views by transforming and merging existing high-definition images with known camera metadata, using perspective transformation and interpolation to create high-quality outputs quickly, suitable for real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-quality rendering techniques are used, then rendering quality is improved, but rendering speed deteriorates
Solution Approach 1:
The system pre-computes and stores multiple reference images of a three-dimensional object from different camera positions and perspectives. When a user requests an arbitrary view, the system retrieves pre-computed reference images that are closest to the requested view, eliminating the need for real-time high-quality rendering while maintaining image quality.
Solution Approach 2:
The system creates a database of reference images that copy and store visual information from multiple perspectives. Instead of generating new high-quality images in real-time, the system retrieves and transforms existing reference images, achieving fast arbitrary view generation without compromising quality.
2Speed
If rasterization is used for fast rendering, then rendering speed is improved, but rendering quality deteriorates
Solution Approach 1:
High-quality reference images are pre-computed and stored in a database before runtime. When arbitrary views are needed, the system retrieves these pre-computed high-quality images rather than generating them in real-time, achieving both speed and quality without relying on rasterization trade-offs.
Solution Approach 2:
The system introduces an intermediary database of reference images that mediates between the request for fast rendering and the need for high quality. This database allows the system to serve both speed and quality requirements by retrieving pre-processed images rather than generating them on-demand.
3Manufacturing precision
If multiple reference images are retrieved and merged, then rendering quality is improved, but computational complexity increases
Solution Approach 1:
Instead of uniformly processing all reference images, the system identifies and uses only the local subset of reference images that are most relevant to the requested arbitrary view. This selective approach maintains high quality for the specific region of interest while reducing overall computational complexity by ignoring unrelated reference images.
Solution Approach 2:
The system changes the parameter of image quantity by retrieving a manageable subset of reference images based on camera position and view parameters. By filtering and selecting only the necessary reference images, the system maintains quality while controlling computational complexity through parameter-based selection rather than processing all available images.
Data Source
AI summary
A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an image comprising a scene are determined by estimating depth values and subsequently refining the estimated depth values at least in part using a machine learning based framework. Determined depth values facilitate generation of other views or perspectives of the scene.