Dynamic Normal Map Update for 3D Moving Object Reconstruction
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Solution Overview
Problem
Current 3D image processing technologies, particularly in artificial reality systems, face challenges in accurately reconstructing and rendering moving objects with fine deformations, such as human bodies, due to limitations in capturing detailed surface features and maintaining resolution during movement.
Innovation Solution
The use of a Red Green Blue and Depth (RGB-D) camera to create dynamic normal maps through the fusion of volumetric Signed Distance Field (SDF) and Non-rigid SDF, updating normal maps in real-time using gradient descent to ensure consistency and detail preservation across different resolutions, allowing for accurate rendering of moving objects with surface deformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D reconstruction methods are used, then the reconstruction process is simple, but the surface details and fine deformations are lost
Solution Approach 1:
The patent segments the 3D reconstruction process into multiple components: volumetric SDF fusion for base geometry, dynamic normal maps for surface details, and multi-resolution pyramidal structures for different viewing distances. This segmentation allows each component to specialize in capturing specific aspects of surface geometry without overwhelming computational complexity
Solution Approach 2:
The patent transitions from traditional 2D image-based reconstruction to 3D volumetric SDF representation, adding a depth dimension that enables accurate capture of surface geometry. The volumetric approach allows surface details to be represented in three-dimensional space rather than projecting them onto two-dimensional images
2Measurement precision
If high-resolution normal maps are used to capture surface details, then surface detail precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamic normal maps that are updated in real-time as the object moves and deforms. Instead of using static high-resolution normal maps that would require extensive pre-computation, the system dynamically adjusts normal map content based on current object pose and deformation, reducing processing time while maintaining detail precision
Solution Approach 2:
The patent updates only the necessary portions of normal maps based on current RGB-D observations rather than re-computing entire high-resolution normal maps. By focusing computational effort on regions with significant changes or highest visual importance, the system maintains surface detail precision while reducing overall processing time
3Measurement precision
If the object moves closer to the camera, then visibility and detail capture are improved, but resolution consistency deteriorates when object distance varies
Solution Approach 1:
The patent segments the normal map representation into a pyramidal multi-resolution structure with multiple levels. Each level captures surface details at different resolutions, allowing the system to select appropriate resolution levels based on object distance while maintaining overall resolution consistency across varying viewing conditions
Solution Approach 2:
The patent dynamically adjusts normal map resolution parameters based on object distance from the camera. When objects are closer, higher resolution levels are used to capture fine details; when objects are farther away, lower resolution levels maintain consistency. This parameter adaptation ensures resolution consistency across varying distances while preserving detail visibility when needed
Data Source
AI summary
In one embodiment, a method includes reconstructing a three-dimensional shape of a target object, creating a two-dimensional normal map for the three-dimensional shape of the target object, accessing image data and depth data associated with the target object, generating a first normal data associated with the target object using the image data and the depth data, updating the normal map using the first normal data, and re-rendering the three-dimensional shape of the target object based on the updated normal map.