Adaptive Multiplane Image Depth Sampling for Memory-Efficient Rendering
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Solution Overview
Problem
Existing methods for generating multiplane images (MPIs) face challenges with uncontrolled memory footprint and inefficient depth layer organization, leading to suboptimal rendering quality and increased memory usage, particularly in deep learning applications.
Innovation Solution
Adaptive depth sampling of MPI layers based on scene geometry, using convolutional neural networks to redistribute empty depth slices and place them at more relevant depths, optimizing memory usage and improving rendering quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number of depth layers in MPI is increased to improve rendering quality, then the memory footprint increases
Solution Approach 1:
The patent applies local quality by making the depth layer distribution non-uniform, concentrating more layers in regions where scene objects are located (foreground) and fewer layers in empty regions (background). This is achieved through adaptive depth sampling that adjusts layer density locally based on scene content, thereby improving rendering quality in critical areas while reducing overall memory footprint.
2Ease of operation
If depth layers are uniformly distributed to simplify organization, then rendering quality decreases due to blur in foreground regions
Solution Approach 1:
The patent applies dynamics by making the depth layer distribution adaptive rather than static. The system dynamically adjusts the positions and densities of depth layers based on scene geometry and object locations, allowing the layer configuration to change according to the specific scene being rendered. This dynamic adaptation resolves the contradiction by maintaining simple uniform sampling methods while achieving non-uniform effective distribution for quality rendering.
3Device complexity
If fixed depth sampling is used to reduce processing complexity, then the system cannot adapt to different scene geometries
Solution Approach 1:
The patent applies preliminary action by performing a preliminary analysis of scene geometry and object locations before finalizing the depth layer configuration. The system first identifies where objects are located in the scene, then uses this information to pre-determine the optimal distribution of depth layers. This preliminary adaptation step allows the system to maintain relatively simple processing while achieving scene-specific optimization.
Data Source
AI summary
Methods and devices for generating a multiplane image from a representation of a 3D scene are disclosed. Given a representation of the 3D scene, for example as a multiview image, a first MPI is generated by a convolutional neural network trained for generating a MPI according to a provided sampling of the depths of the 3D scene. The presence of objects in different layers, that is the complementary of the accumulated transparency of layers, is analyzed to resample the sampling of layers' depth. The new sampling is used by the CNN to generate a new MPI from a representation of the same 3D scene.