References & Further Reading

References

1. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2nd ed., 2004

   The definitive reference on multi-view geometry. Sections 28.1 on epipolar geometry, the fundamental/essential matrix, and bundle adjustment follow this text.

2. J. L. Schonberger and J.-M. Frahm, Structure-from-Motion Revisited, 2016

   Describes the COLMAP SfM pipeline used as the standard preprocessing step for NeRF and 3DGS.

3. B. Triggs, P. F. McLauchlan, R. I. Hartley, and A. W. Fitzgibbon, Bundle Adjustment — A Modern Synthesis, 2000

   Comprehensive treatment of bundle adjustment including the Schur complement trick. Referenced in Section 28.1.

4. J. T. Kajiya, The Rendering Equation, 1986

   The foundational paper introducing the rendering equation. Section 28.2 develops the equation and its differentiable variants.

5. B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, 2020

   Introduces NeRF and the volume rendering integral for neural scene representations. The volume rendering theorem in Section 28.2 follows this paper.

6. H. Kato, D. Beker, M. Morariu, T. Ando, T. Matsuoka, W. Kehl, and A. Gaidon, Differentiable Rendering: A Survey, 2020

   Survey of differentiable rendering methods including SoftRas, neural mesh rendering, and differentiable ray tracing.

7. P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang, NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction, 2021

   NeuS combines neural SDF with volume rendering. Section 28.2 describes the SDF-to-density conversion.

8. J. Hoydis, S. Cammerer, F. A. Aoudia, A. Vem, N. Binder, G. Marcus, and A. Keller, Sionna: An Open-Source Library for Next-Generation Physical Layer Research, 2022

   Introduces Sionna RT, the differentiable ray tracer for wireless channels. Referenced in Sections 28.3 and the engineering note on Sionna RT.

9. G. Caire, On the Illumination and Sensing Model for RF Imaging, TU Berlin Technical Report, 2026

   Unifies diffraction tomography and MIMO radar models under a single forward model. The RF rendering equation in Section 28.3 builds on this framework.

10. D. Feng, C. Haase-Schutz, L. Rosenbaum, H. Hertlein, C. Glaser, F. Timm, W. Wiesbeck, and K. Dietmayer, Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges, 2021

    Comprehensive review of multi-modal fusion for autonomous driving. Section 28.4 on fusion architectures draws on this survey.

11. Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. Rus, and S. Han, BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation, 2023

    Introduces the BEV fusion architecture discussed in Section 28.4.

12. Z. Zhang, T. Luo, Y. Chen, and J. Zhu, NeuRadar: Neural Radiance Fields for Joint Radar-Camera Scene Synthesis, 2023

    Joint neural rendering for radar, camera, and LiDAR. Referenced in Section 28.4 on NeuRadar.

13. J. Gao and G. Caire, Multi-Sensor Data Fusion Approach for RF Imaging, TU Berlin Technical Report (CommIT Group), 2025

    CommIT group's multi-sensor fusion framework: per-sensor back-projection + learned fusion. Referenced in the commit_contribution block of Section 28.4.

14. M. Raissi, P. Perdikaris, and G. E. Karniadakis, Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations, 2019

    The foundational PINN paper. Section 28.5 develops PINNs for the Helmholtz equation following this framework.

15. Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhatt, A. Stuart, and A. Anandkumar, Fourier Neural Operator for Parametric Partial Differential Equations, 2021

    Introduces the FNO architecture. Section 28.5 develops FNO for RF wave propagation and proves the universal approximation theorem for neural operators.

16. T. Cohen and M. Welling, Group Equivariant Convolutional Networks, 2016

    Foundational work on equivariant neural networks. Section 28.5 discusses equivariant architectures for RF imaging.

Further Reading

• Epipolar geometry and the 5-point algorithm

  D. Nister, *An efficient solution to the five-point relative pose problem*, IEEE TPAMI, 2004

  The minimal solver for calibrated two-view geometry, more efficient than the 8-point algorithm for RANSAC-based robust estimation.

• VolSDF and geometry-aware volume rendering

  L. Yariv et al., *Volume Rendering of Neural Implicit Surfaces*, Proc. NeurIPS, 2021

  An alternative to NeuS that derives the density from the SDF via a Laplace CDF, with theoretical analysis of the unbiased surface extraction.

• Differentiable ray tracing for inverse problems

  W. Jakob et al., *Mitsuba 3: A Retargetable Forward and Inverse Renderer*, ACM TOG, 2022

  General-purpose differentiable renderer with support for arbitrary material models and complex light transport, applicable to both optical and RF rendering.

• Radar-camera fusion benchmarks

  H. Caesar et al., *nuScenes: A Multimodal Dataset for Autonomous Driving*, Proc. CVPR, 2020

  The standard benchmark for multi-modal 3D object detection with radar, camera, and LiDAR data.

• Neural operators beyond FNO

  N. Kovachki et al., *Neural Operator: Learning Maps Between Function Spaces with Applications to PDEs*, JMLR, 2023

  Comprehensive survey of neural operator architectures (DeepONet, FNO, graph neural operators) with convergence theory and applications to physics simulation.