Inverse Rendering Visual Material Properties 3D Models
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Solution Overview
Problem
Conventional 3D modeling systems face challenges in accurately depicting visual material properties of objects, such as metalness and roughness, due to inefficiencies and inaccuracies in capturing and rendering these properties, often resulting in unrealistic or inconsistent 3D models.
Innovation Solution
A system that captures images of an object, generates a virtual light source approximating the actual light source, and creates a grid of virtual images with varying visual material property values to identify the best approximation of the actual image, allowing for precise assignment of visual material properties to sections of the object, with the option to refine these properties by narrowing the range of values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Gonioreflectometer is used to directly capture the bidirectional reflectance (BRDF) of a material per pixel, then measurement precision of visual material properties is improved, but productivity deteriorates due to the need to take thousands of images from every possible camera angle and lighting direction
Solution Approach 1:
The system performs preliminary action by capturing a limited set of images under controlled lighting conditions and pre-processing them to extract visual material properties before the actual 3D rendering process. This preliminary extraction of material properties from fewer images resolves the contradiction by preparing data in advance, eliminating the need for thousands of images while maintaining measurement precision.
Solution Approach 2:
The system introduces an intermediary computational process that acts as a mediator between the captured images and the final material property determination. This intermediary process uses image processing algorithms and rendering comparisons to derive accurate visual material properties from a limited set of images, achieving Gonioreflectometer-level precision without the need for thousands of images.
2Device complexity
If default visual material properties are applied to every object, then device complexity is reduced, but manufacturing precision of realistic 3D models deteriorates
Solution Approach 1:
The system applies local quality by determining visual material properties specific to each object or region of an object rather than applying default properties universally. The process captures and analyzes local characteristics of different surfaces, assigning appropriate material properties (such as metalness, roughness, glossiness) to specific regions, thereby achieving manufacturing precision of realistic 3D models without excessive system complexity.
Solution Approach 2:
The system enables self-service by automatically extracting visual material properties from captured images through computational processing, eliminating the need for manual artist estimation while maintaining simplicity. The automated process analyzes image data, compares rendered virtual images with captured images, and assigns material properties without human intervention, achieving both low complexity and high precision.
3Ease of operation
If an artist manually estimates visual material properties based on captured images, then ease of operation is improved, but measurement precision and reliability deteriorate due to inconsistencies and errors
Solution Approach 1:
The system implements feedback by comparing rendered virtual images with actual captured images and using the difference (image error) to iteratively refine visual material property assignments. This feedback loop ensures measurement precision by continuously adjusting material properties until the rendered image closely matches the captured image, eliminating artist estimation errors while maintaining ease of automated operation.
Solution Approach 2:
The system replaces the mechanical system of manual artist estimation with an automated computational process. Instead of relying on human judgment and experience, the system uses image processing algorithms, virtual rendering, and error calculation to objectively determine visual material properties, thereby improving measurement precision and reliability while maintaining operational simplicity through automation.
4Measurement precision
If multiple images are captured from every possible camera angle and lighting direction, then measurement precision of visual material properties is improved, but loss of time and productivity worsen
Solution Approach 1:
The system performs preliminary action by capturing a limited set of images under controlled conditions and pre-processing them to extract visual material properties before the actual 3D rendering process. This preliminary extraction of material properties from fewer images resolves the contradiction by preparing data in advance, eliminating the need for thousands of images while maintaining measurement precision.
Solution Approach 2:
The system introduces an intermediary computational process that acts as a mediator between the captured images and the final material property determination. This intermediary process uses image processing algorithms and rendering comparisons to derive accurate visual material properties from a limited set of images, achieving Gonioreflectometer-level precision without the need for thousands of images.
Data Source
AI summary
Techniques described herein are directed to a system and methods for generating 3D models of an object which accurately depict reflective properties of the object. To do this, an image of an object is captured and a rendered image of the object is generated from the image. The system then generates a lighting effect which approximates an effect of the actual light source on the appearance of the object when the image was captured. A number of rendered images of the object are generated using the lighting effect, each having different visual material property values. Once the rendered images have been generated, the system may compare the generated rendered images to the actual image in order to identify the rendered image which best approximates the actual image. The visual material property values associated with the best approximation are then assigned to the object.