φ-SfT: Shape-from-Template with a Physics-Based Deformation Model
Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods. For the source code and data, see https://4dqv.mpi-inf.mpg.de/phi-SfT/.
Code (1)
Tasks
3D ReconstructionPhysical SimulationsSimilar Papers 제목 키워드 기반
f-SfT: Shape-From-Template With a Physics-Based Deformation Model
Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the u…
3D ReconstructionPhysical SimulationsMeta Deformation Network: Meta Functionals for Shape Correspondence
We present a new technique named "Meta Deformation Network" for 3D shape matching via deformation, in which a deep neural network maps a reference shape onto the parameters of a second neural network whose task is to giv…
DecoderA Linear Least-Squares Solution to Elastic Shape-From-Template
We cast SfT (Shape-from-Template) as the search of a vector field (X,dX), composed of the pose X and the displacement dX that produces the deformation. We propose the first fully linear least-squares SfT method modeling…
PositionSemantic-Aware Implicit Template Learning via Part Deformation Consistency
Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, which solely rely on geometric information,…
Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning
Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object categories remains a significant challenge. In this paper, we prese…