3D Object Reconstruction from Sparse Images Using SVBRDF
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Solution Overview
Problem
Conventional methods for generating 3D geometry and reflectance models for virtual objects from physical objects require dense image sets captured under various viewpoints and lighting conditions, making them impractical for complex objects and scenarios with arbitrary geometry and fine-grained textures, and often result in insufficient reconstruction quality.
Innovation Solution
A two-stage pipeline using a sparse set of images from limited viewpoints and lighting conditions to generate a geometry model and spatially-varying bidirectional reflectance distribution function (SVBRDF) model, employing multi-view geometry estimation and reflectance neural networks, followed by a Poisson reconstruction engine and model optimizer to refine the models for rendering virtual objects from arbitrary viewpoints and lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense image sets are used to capture objects under various viewpoints and lighting conditions, then reconstruction quality is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary action by capturing images under controlled lighting conditions with dedicated light sources positioned at specific locations before reconstruction. This preliminary setup of lighting arrangements enables subsequent reconstruction from sparse viewpoints while maintaining quality, avoiding the need for dense image sets under arbitrary lighting conditions.
Solution Approach 2:
The reconstruction process is segmented into two distinct stages: first capturing images under controlled lighting conditions with known light source positions, then performing reconstruction using this structured data. This segmentation allows the system to achieve high reconstruction quality without requiring dense image sets from all possible lighting conditions.
2Measurement precision
If dense image sets are captured under various viewpoints and lighting conditions, then reconstruction quality is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by capturing images under controlled lighting conditions with dedicated light sources positioned at specific locations before reconstruction. This preliminary setup of lighting arrangements enables subsequent reconstruction from sparse viewpoints while maintaining quality, avoiding the need for dense image sets under arbitrary lighting conditions.
Solution Approach 2:
The system uses periodic action by employing a sequence of discrete lighting conditions with light sources activated at specific time intervals rather than continuous arbitrary lighting. This periodic illumination scheme enables efficient data capture that reduces time consumption while maintaining reconstruction quality.
3Manufacturing precision
If conventional methods are used for complex objects with arbitrary geometry and fine-grained textures, then manufacturing precision is insufficient, but device complexity increases
Solution Approach 1:
The system applies local quality by using spatially-varying bidirectional reflectance distribution functions (SVBRDF) that capture local surface properties at different locations on the object. This enables accurate representation of fine-grained textures and arbitrary geometries with localized surface characteristics, achieving high manufacturing precision without requiring overly complex acquisition systems.
Solution Approach 2:
The system uses parameter changes by modeling surface reflectance properties through SVBRDF parameters that can be adjusted to represent different material characteristics. This parametric approach enables accurate representation of complex objects with varying surface properties without requiring separate measurement systems for each material type.
Data Source
AI summary
Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.