In this talk, I will present a unified and framework to encode any arbitrary spatially grounded 2D shape and 3D object into high-dimensional feature space. The encoding is training-free, general-purpose, mathematically interpretable, and high-frequency-rich, offering a new encoding paradigm that goes against pre-trained neural network encoders or image-based 2D shape encoders. The framework is capable of encoding the object of interest’s geometric structure, spatial pose and optional color information either separately or jointly. When encoding jointly, an emphasis-adjustable strategy is introduced to enable the encoding to flexibly lay different emphasis to each component. I demonstrate the advantage and potential of the framework when the object of interest is spatially grounded.