Differentiable Ray Tracing

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12 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization
Oct 6, 2026Felix Duelmer, Magdalena Wysocki, Nassir Navab +1Differentiable Rendering Ultrasound Imaging
Technical University of Munich & Munich Center for Machine Learning
Direct Optimization of a 3D Finite-Source Reflector via Neural-Network Parameterization
Sep 1, 2026Roel Hacking, Lisa Kusch, Martijn Anthonissen +1Differentiable Ray Tracing
Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands · Signify, High Tech Campus 7, 5656 AE Eindhoven, The Netherlands
LightFuse: Relightable Interactive Gaussian Scene Reconstruction via Multi-Scan Fusion and 2D Gaussian Ray Tracing
Aug 29, 2026Haonan Zhou, Gaoxiang Linghu, Youlin Jia +5Differentiable Ray Tracing 3D Scene Relighting
ZJU-UIUC Institute, Zhejiang University · College of Mathematics, Sichuan University · College of Computer Science and Technology, Zhejiang University +1
Differentiable Voronoi Ray Tracing Beyond Rasterization Speeds
Aug 18, 2026Bernardo Taveira, Carl Lindström, Joakim Johnander +1Differentiable Ray Tracing Novel View Synthesis
Chalmers University of Technology, Sweden · Zenseact, Sweden · Linköping University, Sweden
Inter-Reflective Gaussian Splatting for Robust and Efficient Inverse Rendering
Jul 24, 2026Chun Gu, Xiaofei Wei, Zixuan Zeng +2Inverse Rendering 3D Gaussian Splatting
School of Data Science, Fudan University, and Shanghai Innovation Institute · Shanghai Innovation Institute · School of Data Science, Fudan University
Differentiable Polarized Path Tracing
Jul 14, 2026Pramod Rao, Jérémy Riviere, Xilong Zhou +6Inverse Rendering Differentiable Rendering
Max Planck Institute for Informatics · Saarland Informatics Campus · VIA Research Center +1
Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis
May 8, 2026Niklas Vaara, Lam Huynh, Pekka Sangi +23D Gaussian Splatting Differentiable Rendering
Center for Machine Vision and Signal Analysis, University of Oulu, Finland · CubiCasa Oy, Finland
Power Foam: Unifying Real-Time Differentiable Ray Tracing and Rasterization
Apr 27, 2026Shrisudhan Govindarajan, Daniel Rebain, Dor Verbin +3Differentiable Rendering 3D Scene Representation
Simon Fraser University · Google · Google Deepmind +2
RT-GS: Gaussian Splatting with Reflection and Transmittance Primitives
Apr 1, 2026Kunnong Zeng, Chensheng Peng, Yichen Xie +2Differentiable Ray Tracing 3D Gaussian Splatting
University of Utah · University of California, Berkeley
Stochastic Ray Tracing for the Reconstruction of 3D Gaussian Splatting
Mar 24, 2026Peiyu Xu, Xin Sun, Krishna Mullia +33D Gaussian Splatting Differentiable Ray Tracing
University of Illinois Urbana-Champaign · Adobe Research · Canva Research
Taming Vision Priors for Data Efficient mmWave Channel Modeling
Mar 11, 2026Zhenlin An, Longfei Shangguan, John Kaewell +5Differentiable Ray Tracing Radio Propagation Modeling
University of Georgia · University of Pittsburgh · Interdigital +2
Sionna RT: Technical Report
Apr 30, 2025Fayçal Aït Aoudia, Jakob Hoydis, Merlin Nimier-David +3Differentiable Ray Tracing Wireless Communications

Oct 6, 2026J/K move · Enter open · S save

UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization

Felix Duelmer, Magdalena Wysocki, Nassir Navab, Mohammad Farid Azampour

Technical University of Munich & Munich Center for Machine Learning

Differentiable Rendering Ultrasound Imaging Image Inverse Problems Differentiable Ray Tracing

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Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has so far mainly focused on light transport. We extend this paradigm to medical ultrasound, where image formation resembles transient rendering: echoes are binned by time-of-flight rather than projected onto an image plane. We present UltraDiff, a modular framework for differentiable ultrasound ray tracing. UltraDiff formulates ultrasound image formation as a path-space integral, gated by travel time between the transducer and tissue interfaces, and derives a Monte Carlo estimator of both the forward model and its gradients with respect to scene parameters. We demonstrate this on an inverse geometry estimation: starting from a sphere, an SDF is optimized until simulated echoes match measured ones, recovering vertebral surfaces from simulated B-mode sweeps and from a real robotic acquisition of a spine phantom. Unlike state-of-the-art ultrasound shape reconstruction methods, which rely on pre-segmented images, our approach operates unsupervised on B-mode images through analysis-by-synthesis, while achieving competitive geometric accuracy. Implemented on top of Mitsuba 3, UltraDiff brings differentiable path tracing to a new sensing modality and provides a foundation for inverse problems in acoustic imaging.