3D Head Shape Reconstruction for Hair-Free HRTF Synthesis
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Solution Overview
Problem
Existing 3D scanning techniques for generating head-related transfer functions (HRTFs) are prone to distortion due to acoustically irrelevant surfaces like human hair, leading to inaccurate HRTF synthesis, and manual removal processes are error-prone and inefficient.
Innovation Solution
A machine learning model is employed to generate clean head shapes from 3D scans by using supervised and unsupervised learning methods, specifically generative adversarial networks (GANs), to remove acoustically irrelevant surfaces and ensure anatomically accurate HRTF synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D scanning techniques are used to generate head models for HRTF synthesis, then the process is faster and more scalable, but the accuracy deteriorates due to distortion from acoustically irrelevant surfaces like hair
Solution Approach 1:
The patent extracts and removes acoustically irrelevant surfaces (such as hair) from the 3D scan data, separating them from the acoustically relevant head surfaces. This extraction process allows the system to retain the efficiency of automated 3D scanning while eliminating the distorting elements that compromise HRTF synthesis accuracy
Solution Approach 2:
The patent introduces an intermediary processing step between 3D scanning and HRTF synthesis that identifies and filters out acoustically transparent or irrelevant surfaces. This intermediary layer acts as a mediator that preserves the benefits of automated scanning while correcting the accuracy issues through algorithmic surface classification and removal
2Measurement precision
If manual removal of hair surfaces is performed to improve HRTF accuracy, then the measurement precision improves, but the ease of operation deteriorates due to the painstaking and error-prone nature of the process
Solution Approach 1:
The patent replaces the manual mechanical process of hair removal with an automated computational system. The system uses algorithms to automatically identify, classify, and remove acoustically irrelevant surfaces from 3D scan data, eliminating the need for manual intervention while maintaining or improving accuracy consistency
Solution Approach 2:
The patent enables the system to perform hair removal automatically without human assistance. The computational system self-services by autonomously analyzing the 3D scan data, identifying hair surfaces based on acoustic relevance criteria, and removing them through automated processing pipelines
3Measurement precision
If swimming caps are used during 3D scanning to mitigate hair distortion, then the measurement precision improves, but the ease of operation deteriorates due to the need for special preparation and potential wrinkles
Solution Approach 1:
The patent converts the harmful effect of hair surfaces into a beneficial automated classification process. Rather than requiring subjects to wear swimming caps or undergo manual hair removal, the system uses computational algorithms to identify and separate hair surfaces from head surfaces, turning the original problem into an automated solution that improves both accuracy and ease of operation
Data Source
AI summary
Disclosed embodiments include techniques for generating a clean 3D shape of a portion of the human head of a subject from a 3D scan that includes overlaying surfaces that occlude a portion of the head, where the overlaying surfaces where, the overlaying surfaces are acoustically transparent, such as human hair. These techniques use heuristic constraints to generate a clean 3D shape of a human head for the purpose of generating a more realistic head-related transfer function (HRTF) that is individualized for the subject. The improved HRTF, when applied to a sound system, leads to a more immersive and realistic acoustic experience for the subject when listening to music, experiencing a virtual reality experience, and/or the like.