Point Cloud Gap Filling Using Material Properties
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Solution Overview
Problem
Current image processing techniques for filling gaps in point clouds fail to produce photorealistic results as they rely solely on surrounding color information, ignoring other properties that contribute to the visual characteristics of 3D objects or environments.
Innovation Solution
Systems and methods that generate artificial data points based on material properties and rules defining visual and non-visual relationships, accurately modeling real-world physics and interactions between materials to create photorealistic visual characteristics, motion, and animation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current image processing techniques are used to fill gaps in point clouds, then gaps can be filled with artificially created data points, but the results are not photorealistic because they rely exclusively on surrounding color information and ignore other material properties
Solution Approach 1:
The patent transforms the gap-filling approach by changing from using only color information to incorporating multiple material parameters including roughness, metalness, subsurface scattering, anisotropy, and clearcoat properties. This parameter expansion enables photorealistic rendering by capturing the full physical characteristics of surfaces rather than just visual color data.
Solution Approach 2:
The invention creates a composite data structure for each point that combines multiple material properties (color, roughness, metalness, subsurface scattering, anisotropy, clearcoat) into a unified material representation. This composite approach allows the generated points to faithfully reproduce the complex optical and physical behavior of real-world materials.
2Manufacturing precision
If material properties and rules are used to generate artificial data points, then photorealistic visual characteristics can be achieved, but the system complexity increases due to the need to model real-world physics and interactions
Solution Approach 1:
The patent uses machine learning models trained on real-world material data to learn and replicate the complex relationships between material properties and visual appearance. Instead of implementing complex physics simulations, the system copies the behavior of real materials through learned patterns, achieving photorealism while keeping the processing system more manageable.
Solution Approach 2:
The invention replaces complex mechanical physics simulations with data-driven machine learning approaches. By training models on extensive material datasets, the system substitutes detailed physics modeling with learned statistical relationships, achieving similar visual results with computationally more efficient methods.
3Productivity
If gaps in point clouds are filled using only surrounding color information, then the processing is simple and fast, but the visual characteristics and makeup of the 3D object are not accurately reproduced
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on extensive datasets of material properties and their visual characteristics before the actual gap-filling process. This pre-computation stores learned relationships that can be quickly applied during rendering, enabling fast processing while maintaining high accuracy in material property reproduction.
Solution Approach 2:
The system uses the existing point cloud data itself to train the machine learning models, allowing the data to teach the system about material properties. The learned models then automatically generate realistic material parameters for gap points based on patterns discovered in the existing data, enabling accurate reproduction without manual intervention.
Data Source
AI summary
Disclosed is a system to add photorealistic detail and motion to an image based on a first material property associated with a first set of data points of an incomplete first object, and a second material property associated with a second set of data points of an incomplete second object in the image. The system may generate first artificial data points amongst the first set of data points that completes a first arrangement defined for the first material property, and may generate second artificial data points amongst the second set of data points that completes a second arrangement defined for the second material property. The system may then output an enhanced image of the completed first object based on first set of data points and the first artificial data points, and of the completed second object based on the second set of data points and the second artificial data points.