Denoising of photogrammetric dummy head ear point clouds for individual Head-Related Transfer Functions computation
Individual Head-Related Transfer Functions (HRTFs), crucial for realistic virtual audio rendering, can be efficiently numerically computed from precise three-dimensional head and ear scans. While photogrammetry scanning is promising, it generally lacks accuracy, leading to HRTFs showing significant...
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Zusammenfassung: | Individual Head-Related Transfer Functions (HRTFs), crucial for realistic
virtual audio rendering, can be efficiently numerically computed from precise
three-dimensional head and ear scans. While photogrammetry scanning is
promising, it generally lacks accuracy, leading to HRTFs showing significant
perceptual deviation from reference data, mainly due to scanning errors
affecting the most occluded pinna structures. This paper examines the
application of Deep Neural Networks (DNNs) for denoising photogrammetric ear
scans. Several DNNs, fine-tuned on pinna samples corrupted with synthetic error
modelled to mimic that observed in photogrammetric dummy head scans, are tested
and benchmarked against a classical denoising method. One DNN is further
modified and retrained to enhance its denoising performance. The comparison of
HRTFs derived from original and denoised scans against reference data shows
that the best-performing DNN marginally reduces the deviation of
photogrammetric dummy head HRTFs to levels closer to accurately measured ones.
Additionally, correlation analysis between geometric and HRTF metrics, computed
on the scanned point clouds and their corresponding HRTFs, is used to identify
key measures for evaluating the deviation between target and reference scans.
These findings are expected to guide the selection of relevant loss functions
and foster improvements in this and similar DNN models. |
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DOI: | 10.48550/arxiv.2408.16410 |