# DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes

arXiv: https://arxiv.org/abs/2111.15318 · HTML: https://mlsanity.com/paper/2111.15318/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [MeshSDF: Differentiable Iso-Surface Extraction](https://mlsanity.com/paper/2006.03997/) (2020)
- [ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics](https://mlsanity.com/paper/1810.01054/) (2018)
- [Scalable Differentiable Physics for Learning and Control](https://mlsanity.com/paper/2007.02168/) (2020)
- [ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact](https://mlsanity.com/paper/2007.00987/) (2020)
- [Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction](https://mlsanity.com/paper/1910.01812/) (2019)
- [Learning to Identify Physical Parameters from Video Using Differentiable Physics](https://mlsanity.com/paper/2009.08292/) (2020)
- [Accurately Solving Physical Systems with Graph Learning](https://mlsanity.com/paper/2006.03897/) (2020)

## Built on by (most cited first, 14 of 14)

- [Learning Multi-Object Dynamics with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2202.11855/) (2022)
- [PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation](https://mlsanity.com/paper/2504.16693/) (2025)
- [Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication](https://mlsanity.com/paper/2405.18515/) (2024)
- [Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics](https://mlsanity.com/paper/2502.03449/) (2025)
- [Learning rigid-body simulators over implicit shapes for large-scale scenes and vision](https://mlsanity.com/paper/2405.14045/) (2024)
- [Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?](https://mlsanity.com/paper/2207.05060/) (2022)
- [One-Shot Real-to-Sim via End-to-End Differentiable Simulation and Rendering](https://mlsanity.com/paper/2412.00259/) (2024)
- [Improving Gradient Computation for Differentiable Physics Simulation with Contacts](https://mlsanity.com/paper/2305.00092/) (2023)
- [Physics-Based Rigid Body Object Tracking and Friction Filtering From RGB-D Videos](https://mlsanity.com/paper/2309.15703/) (2023)
- [Bayesian Differentiable Physics for Cloth Digitalization](https://mlsanity.com/paper/2402.17664/) (2024)
- [Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators](https://mlsanity.com/paper/2409.13228/) (2024)
- [Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing](https://mlsanity.com/paper/2503.19893/) (2025)
- [Rigid Body Adversarial Attacks](https://mlsanity.com/paper/2502.05669/) (2025)
- [URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification](https://mlsanity.com/paper/2606.18861/) (2026)

## Abstract (arXiv)

Differentiable physics is a powerful tool in computer vision and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are known in advance. In this paper, we propose a novel approach to differentiable physics with frictional contacts which represents object shapes implicitly using signed distance fields (SDFs). Our simulation supports contact point calculation even when the involved shapes are nonconvex. Moreover, we propose ways for differentiating the dynamics for the object shape to facilitate shape optimization using gradient-based methods. In our experiments, we demonstrate that our approach allows for model-based inference of physical parameters such as friction coefficients, mass, forces or shape parameters from trajectory and depth image observations in several challenging synthetic scenarios and a real image sequence.