Compact 3D Representation

Compact 3D representation research aims to efficiently encode complex three-dimensional shapes and objects using minimal data, enabling faster processing and reduced storage needs. Current efforts focus on developing novel architectures, such as transformer networks and tensor trains, often incorporating techniques like masked autoencoders and vector quantization to achieve high-fidelity representations from sparse data. These advancements are improving performance in applications ranging from 3D reconstruction and novel view synthesis to drug discovery and molecular design, where efficient handling of complex 3D structures is crucial.