<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhu, Houlin</style></author><author><style face="normal" font="default" size="100%">Xihong Wu</style></author><author><style face="normal" font="default" size="100%">Tianshu Qu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gaussian Splatting-Based Head and Pinna Reconstruction for Individualized HRTF Computation from Commodity Multi-View Images</style></title><secondary-title><style face="normal" font="default" size="100%">the AES 160th Convention</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%"> 28-30 May</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://aes.org/publications/elibrary-page/?id=23188</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Copenhagen, Denmark</style></pub-location><pages><style face="normal" font="default" size="100%">10290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Individualized head-related transfer functions (HRTFs) require accurate pinna geometry, yet commodity multi-view captures leave the ear region self-occluded and weakly textured. We present a practical pipeline that couples ear-centric acquisition with 3D Gaussian splatting (3DGS) and the boundary element method (BEM) for complete HRTF computation. The protocol augments horizontal views with per-ear elevated captures under directional lighting; 3DGS training with depth-distortion regularization yields watertight meshes via truncated signed distance function (TSDF) fusion. Standardized head coordinates and ear-canal annotations interface the mesh with BEM. Experimental evaluations demonstrate that our method achieves lower ear-region geometric error and lowerfull-band spectral distortion compared to existing image-based personalized reconstruction baselines including AudioEar, NeuS, and Metashape MVS.</style></abstract></record></records></xml>