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Research map / 3D vision / Depth and stereo

MonoPhysics: Estimating Geometry, Appearance, and Physical Parameters from Monocular Videos

NeurIPS 2026 · 3D vision › Depth and stereo · approach: Feed-forward 3D reconstruction · 0 citations (OpenAlex)

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Proposes MonoPhysics, a monocular inverse-physics framework that jointly optimizes 3DGS geometry, appearance, and MPM material parameters using scale reparameterization, physics-aware geometry refinement, and a differentiable position map.

Problem

Recovering physical parameters and future dynamics of deformable objects from a single-camera video, where multi-view constraints that resolve scale and 3D geometry are absent.

Approach

Operates in camera space with a single learnable global scale scalar, makes per-particle volumes adaptive via iterative partition-of-unity, uses physical importance in particle relocation, and adds a position-map reparameterization that gives pixel-coordinate gradients to Gaussians, all coupled through a differentiable MPM simulator on 3DGS particles.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Vid2Sim Google Scanned Objects SpringGaus real captures

Benchmarks: Vid2Sim MonoPhysics synthetic dataset SpringGaus real captures

Metrics: PSNR SSIM LPIPS Chamfer Distance Earth Mover's Distance MAE log E MAE Poisson's ratio MAE log sigma_y MAE initial velocity

Baselines: PAC-NeRF SpringGaus GIC Vid2Sim Oracle Midpoint

Builds on: PAC-NeRF SpringGaus GIC Vid2Sim 3DGS-MCMC

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