Deformable Shape Reconstruction
Overview
Deformable shape reconstruction studies how to recover the changing geometry of objects such as tissue, cloth, hands, and soft robots from incomplete observations. Methods use deformable meshes and templates, neural implicit fields and Gaussian splats, or sensor-based shape estimates, often combining visual evidence with physical or geometric constraints. The work supports applications including surgical navigation, robotic manipulation, animation, and simulation, with growing attention to real-time reconstruction and prediction. A central challenge is recovering accurate, physically plausible shapes despite occlusion, sparse views, camera motion, and differences between training objects and objects encountered in practice.
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