Neural Rendering Scene Representation via Adaptive Mesh Layers
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Solution Overview
Problem
Existing neural rendering methods using semi-transparent multi-layer representations are limited by a small number of layers due to resource constraints, leading to coarse approximation of scene geometry, which affects novel view synthesis quality and introduces artifacts.
Innovation Solution
A method that builds a scene-adaptive geometric proxy using a small number of mesh layers with continuous depth coordinates, where two deep neural networks are jointly trained in an end-to-end fashion to predict the layered structure and estimate color and opacity values, improving the accuracy of novel view synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a small number of layers is used in multi-layer representation, then resource consumption is reduced and rendering is faster, but the geometry approximation becomes coarse and synthesis quality deteriorates
Solution Approach 1:
The patent changes the parameters of the layer representation from fixed regular grids to scene-adaptive meshes with continuous depth coordinates. This allows the geometry to be optimized for each specific scene while maintaining a manageable number of layers, resolving the contradiction between rendering speed and geometry accuracy.
Solution Approach 2:
The patent introduces dynamic scene-adaptivity where the layer geometry is not fixed but adjusts to match the specific scene structure. The mesh layers dynamically reconfigure themselves based on the input images, enabling accurate geometry representation with fewer layers compared to static regular grids.
2Measurement precision
If a large number of layers is used to improve geometry approximation, then synthesis quality improves, but resource constraints are exceeded and overfitting occurs
Solution Approach 1:
The patent optimizes the number of layers by changing from fixed large numbers to a smaller adaptive number that is sufficient for each scene. The scene-adaptive mechanism automatically determines the appropriate number of layers needed, preventing overfitting while maintaining accuracy.
Solution Approach 2:
The patent uses neural networks to learn and copy the essential geometric structure from input images directly into the layer representation. This copying process enables accurate geometry capture without requiring a large number of layers, as the neural network extracts and represents only the necessary geometric information.
3Ease of manufacture
If regular grids of surfaces are used for multi-layer representation, then implementation is simpler and rendering is faster, but the representation cannot adapt to different scene geometries and synthesis accuracy suffers
Solution Approach 1:
The patent transitions from static regular grids to dynamic scene-adaptive meshes. The mesh structure dynamically adjusts to match the specific scene geometry, enabling the system to adapt to various scene types while maintaining implementation simplicity through a unified neural network framework.
Solution Approach 2:
The patent changes the geometric parameters from fixed regular grids to variable mesh structures with continuous depth coordinates. This parameter change enables the representation to adapt to different scene geometries while the neural network framework maintains implementation simplicity through learned transformations.
4Shape
If non-learned post-processing merging is applied to excessive spheres, then scene-adapted geometry is created, but the merging step is not incorporated into learning and degrades new view synthesis quality
Solution Approach 1:
The patent merges the geometry prediction and coloring operations into a unified end-to-end trained system. Instead of separate post-processing steps, both operations are integrated and trained together, ensuring that the scene-adapted geometry and material properties are optimized for their combined effect on novel view synthesis quality.
Solution Approach 2:
The patent incorporates feedback through end-to-end training where the output of the geometry prediction is fed back into the coloring network and vice versa. This feedback mechanism ensures that both geometry and material estimates are continuously optimized for their joint impact on synthesis quality, eliminating the degradation issue of separate post-processing steps.
Data Source
AI summary
The present disclosure relates to the field of artificial intelligence (AI) and neural rendering, and particularly to a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion and to a computing device implementing the method. The method of generating a multi-layer representation of a scene includes: obtaining a pair of images of the scene, the pair of the images comprising a reference image and a source image; performing a reprojection operation on the pair of images to generate a plane-sweep volume; predicting, using a geometry network, a layered structure of the scene based on the plane-sweep volume; and estimating, using a coloring network, color values and opacity values for the predicted layered structure of the scene to obtain the multi-layer representation of the scene; wherein the geometry network and the coloring network are trained in end-to-end manner.