3D Decomposition

3D decomposition focuses on breaking down complex 3D scenes or objects into simpler, more manageable components for easier analysis, manipulation, and understanding. Current research emphasizes automated methods using neural implicit surfaces, transformer architectures (like TPVFormer), and optimization techniques (such as ADMM) to achieve this decomposition, often integrating 2D image information and leveraging techniques like region growing and clustering. These advancements are significantly impacting fields like robotics (through improved inverse kinematics), computer vision (via enhanced scene reconstruction and object segmentation), and autonomous driving (by improving 3D scene perception and prediction). The resulting decomposed representations facilitate downstream tasks such as animation, scene editing, and multi-agent collaboration.