Parametric SDF for Dynamic Surface Reconstruction

Reconstructing high-fidelity surfaces of dynamic scenes re-mains a critical challenge. While current methods excel at novel viewsynthesis, they often struggle to recover accurate and stable geometry,particularly under large non-rigid deformations. This results in noisymeshes that are unsuitable for downstream simulation or editing. In thiswork, we introduce a new paradigm for dynamic surface reconstructionbased on a parametric Signed Distance Function (p-SDF). Our key in-sight is to generalize static SDF fields—where each spatial point stores aconstant value—into time-dependent parametric curves where each curvemodels a temporally evolving SDF trajectory. This parametric SDF rep-resentation provides a principled way to capture complex temporal varia-tions, naturally enforcing smoothness and continuity in shape dynamics.At each timestamp, a static SDF field can be queried from p-SDF andconverted into an explicit surface mesh via differentiable iso-surfacing.By rendering these meshes with a physically based differentiable ren-derer, we optimize the underlying parametric curves end-to-end against2D image observations. Our framework produces high-fidelity surfacesand inherently disentangles geometry, material, and lighting from multi-view videos. It robustly reconstructs geometry under large-scale motionsand resolves appearance ambiguities caused by lighting and occlusions.Experiments on both synthetic and real-world scenes demonstrate thatour method achieves state-of-the-art geometric accuracy. The projectpage for p-SDF is available at https://timchonggao.github.io/psdf.