Relightable 3D Reconstruction Using Diffusion and Latent NeRF

Solution Overview

Problem

Traditional methods for relighting and novel view synthesis in computer graphics are computationally expensive and prone to inaccuracies due to the complexity of inverse rendering, which struggles with determining geometry, materials, and lighting ambiguity, leading to unreliable renderings under varied lighting conditions.

Innovation Solution

A method utilizing a machine-learned relighting diffusion model and latent neural radiance field to generate high-quality synthetic images under novel lighting conditions, leveraging advanced machine learning techniques to handle complex lighting scenarios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional inverse rendering methods are used for relighting and novel view synthesis, then the process can generate images under new lighting conditions and viewpoints, but the computational cost becomes excessively high and the results become inaccurate due to ambiguity in geometry, materials, and lighting

Engineering Contradiction:

Improveaccuracy of renderingVSAvoidcomputational cost

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent creates a synthetic training dataset by rendering 3D models under various lighting conditions and uses this copied data to train a diffusion model. This allows the system to learn accurate rendering relationships without performing expensive real-time inverse rendering, thus improving reliability while reducing computational cost.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the traditional mechanical inverse rendering process with a machine learning-based diffusion model that has been pre-trained on synthetic data. This substitution eliminates the need for complex iterative optimization and Monte Carlo rendering during inference, dramatically reducing computational requirements while maintaining or improving rendering accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If inverse rendering is used to infer geometry, materials, and lighting from images, then the system can reconstruct scene properties, but the process becomes brittle and ambiguous leading to inaccuracies under novel lighting conditions

Engineering Contradiction:

Improveconsistency of reconstructionVSAvoidcomplexity of rendering pipeline

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent performs preliminary training of the diffusion model on synthetic data generated from 3D models with known properties. This pre-training establishes robust relationships between 3D scene properties and 2D image appearances under various lighting conditions, enabling reliable and consistent reconstruction without complex iterative optimization during actual use.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a synthetic training dataset by rendering 3D models under various lighting conditions and uses this copied data to train a diffusion model. This allows the system to learn accurate rendering relationships without performing expensive real-time inverse rendering, thus improving reliability while reducing computational cost.

Inventive Principle:

Principle #26Copying

3Productivity

If diffusion models are used for relighting, then computational costs are reduced and quality is improved, but the process requires training on synthetic data which adds complexity to the workflow

Engineering Contradiction:

Improvespeed of image generationVSAvoidcomplexity of data preparation

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent creates a synthetic training dataset by rendering 3D models under various lighting conditions and uses this copied data to train a diffusion model. This allows the system to learn accurate rendering relationships without performing expensive real-time inverse rendering, thus improving reliability while reducing computational cost.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent changes the approach from direct physical rendering to a data-driven statistical model. By training the diffusion model on synthetic data with known parameters, the system can quickly generate high-quality images by sampling from the learned distribution, achieving fast inference without requiring complex real-time rendering pipelines.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250378633A1Relightable 3D Reconstruction and View Synthesis

Publication Date: 2025.12.11 GOOGLE LLC

AI summary

Provided are systems and methods for relightable view synthesis that can process a set of source images captured under unknown lighting conditions to produce 3D reconstructions under novel target lighting and from novel viewpoints or poses. Initially, an example method includes obtaining source images and target lighting data, followed by generating radiance data using a source neural scene representation and a rendering engine. A machine-learned relighting diffusion model can then be employed to process the source images and radiance data to generate re-lit images. These images are subsequently used to train a latent neural radiance field model, which, upon querying following training, can generate synthetic images from novel poses under the target lighting. The proposed technology can be beneficial for applications in virtual reality, filmmaking, game development, and other settings, offering a robust alternative to traditional inverse rendering methods by leveraging advanced machine learning techniques to handle complex lighting scenarios.