3D Scene Reconstruction Model Using Shared RGBD Geometry and Texture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing scene reconstruction techniques require training a different neural network from scratch for each scene, leading to high processing and memory requirements due to the complexity and number of learnable parameters needed for generating 3D representations.
Innovation Solution
A single trained neural network is used to generate 3D representations for multiple scenes by mapping a single RGBD image and viewpoint, utilizing a pre-trained geometry and texture encoders/decoders, reducing the need for multiple neural network training and parameter storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a different neural network is trained from scratch for each scene, then the 3D representation can be generated for each scene, but the processing resources and memory required become prohibitive
Solution Approach 1:
The patent applies universality by training a single neural network to generate 3D representations for multiple different scenes. The neural network is trained to take as input a set of source images from different viewpoints and output a 3D representation, making it capable of handling various scenes without requiring separate networks for each scene. This reduces the overall system complexity while maintaining the ability to process diverse scenes.
Solution Approach 2:
The patent segments the neural network into distinct functional components: a geometry encoder that processes depth information, a geometry decoder that generates 3D structure, and a texture encoder/decoder that handles appearance information. This segmentation allows each component to be optimized independently and facilitates more efficient training and inference, reducing the computational burden while maintaining scene reconstruction capability.
2Manufacturing precision
If a complex neural network with vast number of learnable parameters is used, then effective 3D representation can be generated, but the processing resources and memory required become prohibitive
Solution Approach 1:
The neural network is divided into specialized modules: geometry encoders/decoders for structural information and texture encoders/decoders for appearance information. This segmentation allows the model to achieve high-quality 3D representations by combining precise geometric understanding with accurate texture mapping, while reducing the overall parameter count compared to a monolithic network approach.
Solution Approach 2:
Different parts of the neural network are designed with different levels of complexity appropriate to their function. The geometry components use fewer parameters optimized for structural accuracy, while texture components use additional parameters for appearance fidelity. This local quality differentiation allows high overall precision without requiring uniformly high complexity throughout the entire network.
Data Source
AI summary
In various embodiments, a training application trains a machine learning model to generate three-dimensional (3D) representations of two-dimensional images. The training application maps a depth image and a viewpoint to signed distance function (SDF) values associated with 3D query points. The training application maps a red, blue, and green (RGB) image to radiance values associated with the 3DI query points. The training application computes a red, blue, green, and depth (RGBD) reconstruction loss based on at least the SDF values and the radiance values. The training application modifies at least one of a pre-trained geometry encoder, a pre-trained geometry decoder, an untrained texture encoder, or an untrained texture decoder based on the RGBD reconstruction loss to generate a trained machine learning model that generates 3D representations of RGBD images.