Interpolation has been applied in multiple fields such as geographic research and image resampling [1][2]. When speaking of interpolationnot limited to its application in HRTFit generally refers to constructing a function that passes exactly through all collected data points. Numerous methods have been developed for measuring Head- Related Transfer Functions (HRTFs) [3]. One of the key goals in these developments has been improving efficiency, which can be enhanced through the use of interpolation techniques [3]. Additionally, since HRTF measurements are inherently limited to discrete spatial positions, interpolation is commonly employed to estimate values at positions between the measured points [3]. This paper is a primer review of the existing HRTF interpolation strategies including global interpolation methods and local interpolation methods. Additionally, the use of various data-driven and deep learning techniques is extensively discussed to provide the latest development of such a topic.
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