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docs/publications/focal_surface_light_transport.md

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+# Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions
+
+## People
+<table class=""  style="margin: 10px auto;">
+  <tbody>
+    <tr>
+      <td> <img src="../../people/chuanjun_zheng.png" width="120" alt=/> &nbsp;&nbsp;&nbsp;&nbsp;</td>
+      <td> <img src="../../people/yicheng_zhan.png" width="120" alt=/> &nbsp;&nbsp;&nbsp;&nbsp;</td>
+      <td> <img src="../../people/liang_shi.png" width="120" alt=/> &nbsp;&nbsp;&nbsp;&nbsp;</td>
+      <td> <img src="../../people/ozan_cakmakci.png" width="120" alt=/> &nbsp;&nbsp;&nbsp;&nbsp;</td>
+      <td> <img src="../../people/kaan_aksit.png" width="120" alt=/> &nbsp;&nbsp;&nbsp;&nbsp;</td>
+    </tr>
+    <tr>
+      <td><p style="text-align:center;"><a href="https://scholar.google.com.hk/citations?user=9Jk_LC8AAAAJ&hl=zh-CN">Chuanjun Zheng</a><sup>1</sup></p></td>
+      <td><p style="text-align:center;"><a href="https://scholar.google.com/citations?hl=zh-CN&user=x2ptSYUAAAAJ">Yicheng Zhan</a><sup>1</sup></p></td>
+      <td><p style="text-align:center;"><a href="https://people.csail.mit.edu/liangs/">Liang Shi</a><sup>2</sup></p></td>
+      <td><p style="text-align:center;"><a href="https://scholar.google.com/citations?user=xZLjeAMAAAAJ&hl=en">Ozan Cakmakci</a><sup>3</sup></p></td>
+      <td><p style="text-align:center;"><a href="https://kaanaksit.com">Kaan Akşit</a><sup>1</sup></p></td>
+    </tr>
+  </tbody>
+</table>
+<p style="text-align:center;">
+<sup>1</sup>University College London,
+<sup>2</sup>Massachusetts Institute of Technology,
+<sup>3</sup>Google
+</p>
+<p style="text-align:center;"><b>SIGGRAPH Asia 2024 Technical Communications </b></p>
+
+## Resources
+:material-newspaper-variant: [Manuscript](https://kaanaksit.com/assets/pdf/ZhengEtAl_SigAsia2024_Focal_surface_holographic_light_transport_using_learned_spatially_adaptive_convolutions.pdf)
+:material-newspaper-variant: [Supplementary](https://kaanaksit.com/assets/pdf/ZhengEtAl_SigAsia2024_Supplementary_Focal_surface_holographic_light_transport_using_learned_spatially_adaptive_convolutions.pdf)
+
+[//]: # (:material-file-code: [Code]&#40;https://github.com/complight/multicolor&#41;)
+
+[//]: # (:material-video-account: [Project video]&#40;https://kaanaksit.com/assets/video/KavakliSigAsia2023Multicolor.mp4&#41;)
+??? info ":material-tag-text: Bibtex"
+        @inproceedings{kavakli2023multicolor,
+          title={Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions},
+          author={Chuanjun Zheng, Yicheng Zhan, Liang Shi, Ozan Cakmakci, and Kaan Akşit},
+          booktitle = {SIGGRAPH Asia 2024 Technical Communications (SA Technical Communications ’24)},
+          keywords = {Computer-Generated Holography, Light Transport, Optimization},
+          location = {Tokyo, Japan},
+          series = {SA '24},
+          month={December},
+          year={2024},
+          doi={https://doi.org/10.1145/3681758.3697989}
+        }
+
+
+[//]: # (## Video)
+
+[//]: # (<video controls>)
+
+[//]: # (<source src="https://kaanaksit.com/assets/video/KavakliSigAsia2023Multicolor.mp4" id="“ type="video/mp4">)
+
+[//]: # (</video>)
+
+
+## Abstract
+Computer-Generated Holography (CGH) is a set of algorithmic methods for identifying holograms that reconstruct Three-Dimensional 
+scenes in holographic displays. CGH algorithms decompose 3D scenes into multiplanes at different depth levels and rely on simulations
+of light that propagated from a source plane to a targeted plane. Thus, for $n$ planes, CGH typically optimizes holograms using $n$ plane-to-plane 
+light transport simulations, leading to major time and computational demands. Our work replaces multiple planes with a focal surface and introduces 
+a learned light transport model that could propagate a light field from a source plane to the focal surface in a single inference. Our model leverages 
+spatially adaptive convolution to achieve depth-varying propagation demanded by targeted focal surfaces. The proposed model reduces the hologram 
+optimization process up to $1.5x$, which contributes to hologram dataset generation and the training of future learned CGH models.
+
+
+## Focal Surface Holographic Light Transport
+Simulating light propagation among multiple planes in a 3D volume is computationally 
+demanding, as a 3D volume is represented with multiple planes and each plane requires
+a separate calculation of light propagation to reconstruct the target image. Thus, 
+for $n$ planes, conventional light transport simulation methods require $n$ plane-to-plane 
+simulations, leading to major time and computational demands. Our work replaces multiple
+planes with a focal surface and introduces a learned light transport model that could 
+propagate a light field from a source plane to the focal surface in a single inference,
+reducing simulation time by $10x$.
+<figure markdown>
+  ![Image title](media/focal_surfaec_lightprop_proposed_vs_conv.png){ width="500" }
+</figure>
+
+## Results
+When simulating a full-color, all-in-focus 3D image across a focal surface, conventional 
+Angular Spectrum Method (ASM) requires eighteen forward
+passes to simulate the 3D image with six depth planes.
+In contrast, our model simulates the three colorprimary images simultaneously
+onto a focal surface with a single forward pass. 
+In the mean time, our model preserves more high-frequency content than U-Net, providing 
+finer details and sharper edges, closer to the ground truth. 
+<figure markdown>
+  ![Image title](media/focal_surface_lightprop_experimental_results_castle.png){ width="800" }
+</figure>
+
+ We utilize our model for a 3D phase-only hologram optimization application under
+ $0 mm$ propagation distance. Optimizing holograms with six target planes using ASM
+ is denoted as ASM 6, while Ours 6 represents optimizing holograms using our model with six
+ focal surfaces. When comparing the simulation results, all holograms are reconstructed using ASM for performance assessment. 
+Ours 6 achieves comparable results with about $70\%$ of the optimization time compared to ASM 6.
+
+<figure markdown>
+  ![Image title](media/focal_surface_lightprop_experimental_results_leaves.png){ width="800" }
+</figure>
+
+We also apply our model for a 3D phase-only hologram optimization application under $10 mm$ propagation distance.
+
+<figure markdown>
+  ![Image title](media/focal_surface_lightprop_experimental_results_tiger.png){ width="800" }
+</figure>
+
+
+
+
+## Relevant research works
+Here are relevant research works from the authors:
+
+- [Multi-color Holograms Improve Brightness in Holographic Displays](multi_color.md)
+- [HoloBeam: Paper-Thin Near-Eye Displays](holobeam.md)
+- [Realistic Defocus for Multiplane Computer-Generated Holography](realistic_defocus_cgh.md)
+- [Optimizing Vision and Visuals: Lectures on Cameras, Displays, and Perception](../teaching/siggraph2022_optimizing_vision_and_visuals.md)
+- [Learned Holographic Light Transport](https://github.com/complight/realistic_holography)
+- [Metameric Varifocal Holograms](https://github.com/complight/metameric_holography)
+- [Odak](https://github.com/kunguz/odak)
+
+
+[//]: # (## External Other Links)
+
+[//]: # (Here are links related to our project such as videos, articles or podcasts:)
+
+[//]: # ()
+[//]: # (- [ACM SIGGRAPH Asia 2023, Technical Papers Fast Forward &#40;Preview the presentations on 13 Dec, Day 2&#41;]&#40;https://youtu.be/dMsD_xXOEKA?feature=shared&t=332&#41;)
+
+
+## Outreach
+We host a Slack group with more than 250 members.
+This Slack group focuses on the topics of rendering, perception, displays and cameras.
+The group is open to public and you can become a member by following [this link](../outreach/index.md).
+
+## Contact Us
+!!! Warning
+    Please reach us through [email](mailto:chuanjunzhengcs@gmail.com) to provide your feedback and comments.
+
+
+
+

mkdocs.yml

@@ -31,6 +31,7 @@ nav:
   - Publications:
     - List of publications: 'publications/index.md'
     - Highlighted works:
+      - 'Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions': 'publications/focal_surface_light_transport.md'
       - 'SpecTrack: Learned Multi-Rotation Tracaking via Speckle Imaging': 'publications/spec_track.md'
       - 'Autocolor: Learned Light Power Control for Multi-Color Holograms': 'https://complightlab.com/autocolor_'
       - 'Multi-color Holograms Improve Brightness in Holographic Displays': 'publications/multi_color.md'