# Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin

arXiv: https://arxiv.org/abs/2603.18026 · HTML: https://mlsanity.com/paper/2603.18026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 96 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [Solving PDE-constrained Control Problems Using Operator Learning](https://mlsanity.com/paper/2111.04941/) (2021)
- [Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction](https://mlsanity.com/paper/1901.05567/) (2019)
- [Learning Physical Constraints with Neural Projections](https://mlsanity.com/paper/2006.12745/) (2020)
- [Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal](https://mlsanity.com/paper/2111.06195/) (2021)
- [Invertible Neural BRDF for Object Inverse Rendering](https://mlsanity.com/paper/2008.04030/) (2020)
- [GenDR: A Generalized Differentiable Renderer](https://mlsanity.com/paper/2204.13845/) (2022)
- [Radio Frequency Ray Tracing with Neural Object Representation](https://mlsanity.com/paper/2411.18635/) (2024)
- [InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin](https://mlsanity.com/paper/2508.14204/) (2025)
- [High Resolution Millimeter Wave Imaging For Self-Driving Cars](https://mlsanity.com/paper/1912.09579/) (2019)
- [3D Human Pose Estimation for Free-from and Moving Activities Using WiFi](https://mlsanity.com/paper/2204.07878/) (2022)

## Built on by (most cited first, 1 of 1)

- [TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements](https://mlsanity.com/paper/2609.32923/) (2026)

## Abstract (arXiv)

Digital twins, virtual simulated replicas of physical scenes, are transforming system design across industries. However, their potential in radio frequency (RF) systems has been limited by the non-differentiable nature of conventional RF simulators. The visibility of propagation paths causes severe discontinuities, and differentiable rendering techniques from computer graphics cannot easily transfer due to point-source antennas and dominant specular reflections. In this paper, we present RFDT, a physically based differentiable RF simulation framework that enables gradient-based interaction between virtual and physical worlds. RFDT resolves discontinuities with a physically grounded edge-diffraction transition function, and mitigates non-convexity from Fourier-domain processing through a signal domain transform surrogate. Our implementation demonstrates RFDT's ability to accurately reconstruct digital twins from real RF measurements. Moreover, RFDT can augment diverse downstream applications, such as test-time adaptation of machine learning-based RF sensing and physically constrained optimization of communication systems.