3D Surface Reconstruction via Depth Sensor Fusion and Adaptive Smoothing
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Solution Overview
Problem
Current 3D surface reconstruction technologies for augmented reality struggle to produce hole-free and fold-free surface models efficiently, often compromising on edge accuracy or sensing range, and require human intervention or extensive processing time.
Innovation Solution
The implementation of a probabilistic fusion and depth resolution optimization technology that uses depth sensor fusion, adaptive smoothing, conditional iterative manifold interpolation, and triangular edge contraction to generate high-quality, compact 3D surface models with seamless edge preservation and expanded sensing range, enabling automatic and parameter-free processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current 3D surface reconstruction technologies are used, then surface models can be generated, but the models contain holes and folds, and edge accuracy is compromised
Solution Approach 1:
The patent performs preliminary depth sensor fusion and adaptive smoothing before surface reconstruction to pre-process depth data, removing noise and filling gaps in advance. This preliminary action ensures that the subsequent reconstruction process works with cleaned depth information, preventing holes and folds in the final model while preserving edge accuracy through the adaptive nature of the smoothing algorithm.
Solution Approach 2:
The patent introduces conditional iterative manifold interpolation as an intermediary process between depth sensing and final model generation. This interpolation technique acts as a mediator that fills missing surface data by referencing surrounding manifold structures, ensuring topological consistency and eliminating holes without compromising the accuracy of existing edge features.
2Reliability
If current 3D surface reconstruction technologies are used, then surface models can be generated, but extensive processing time is required
Solution Approach 1:
The patent implements self-tuning bandwidth estimation in the adaptive smoothing algorithm, which automatically adjusts its parameters based on local surface characteristics without requiring manual intervention or iterative refinement. This self-service mechanism eliminates the need for time-consuming parameter tuning while maintaining high surface model quality, significantly reducing processing time.
Solution Approach 2:
The patent dynamically changes smoothing bandwidth parameters based on local geometric features and depth data quality. By adapting parameters to local conditions rather than using fixed values, the system achieves high-quality reconstruction in fewer processing passes, improving productivity without sacrificing model reliability.
3Measurement precision
If current 3D surface reconstruction technologies are used, then surface models can be generated, but sensing range is limited
Solution Approach 1:
The patent performs depth sensor fusion that combines depth information from multiple sensing dimensions and temporal frames. By integrating data across multiple dimensions (spatial, temporal, and angular), the system extends its effective sensing range while maintaining measurement precision through the fusion of complementary depth observations from different perspectives.
Data Source
AI summary
An embodiment of a semiconductor package apparatus may include technology to perform depth sensor fusion to determine depth information for a surface, smooth the depth information for the surface and preserve edge information for the surface based on adaptive smoothing with self-tuning band-width estimation, iteratively remove holes from the surface based on conditional iterative manifold interpolation, reduce one or more of a file size and an on-memory storage size of data corresponding to the surface based on triangular edge contraction, and construct at least a portion of a 3D model based on data corresponding to a visible portion of the surface. Other embodiments are disclosed and claimed.