upload mprnn

_pages/about.md

@@ -29,7 +29,15 @@ I'm Yiqian Wu, a second-year (2021-now) Ph.D. student in State Key Lab of CAD&CG
 
    Arxiv, 2023
 
-   Yiqian Wu, Jing Zhang, Hongbo Fu, Xiaogang Jin.
+   **Yiqian Wu**, Jing Zhang, Hongbo Fu, Xiaogang Jin.
+
+2. [Deep Real-time Volumetric Rendering Using Multi-feature Fusion](https://onethousandwu.com/publication/mprnn)
+
+   Proceedings of Siggraph'2023, Los Angeles, 6-10 August, 2023.
+
+   Jinkai Hu, Chengzhong Yu, Hongli Liu, [Ling-qi Yan](https://sites.cs.ucsb.edu/~lingqi/index.html), **Yiqian Wu**, [Xiaogang Jin](http://www.cad.zju.edu.cn/home/jin)
+
+
 
 ## 2022
 

_publications/mprnn.md

@@ -0,0 +1,35 @@
+---
+title: "Deep Real-time Volumetric Rendering Using Multi-feature Fusion"
+collection: publications
+permalink: /publication/mprnn
+excerpt: 'Jinkai Hu, Chengzhong Yu, Hongli Liu, [Ling-qi Yan](https://sites.cs.ucsb.edu/~lingqi/index.html), **Yiqian Wu**, [Xiaogang Jin](http://www.cad.zju.edu.cn/home/jin)'
+date: 2023-08-30
+venue: 'Proceedings of Siggraph'2023, Los Angeles, 6-10 August, 2023.'
+paperurl: 'https://sites.cs.ucsb.edu/~lingqi/publications/paper_mrpnn.pdf'
+citation: 'coming soon'
+code: 'coming soon'
+video: 'coming soon'
+supplementary_materials: 'coming soon'
+project_page: 'coming soon'
+year: '2023'
+---
+![mprnn](http://oneThousand1000.github.io/images/publications/mprnn.png)
+
+<b>Abstract:</b>
+
+We present Multi-feature Radiance-Predicting Neural Networks (MRPNN), a practical framework with a lightweight feature fusion neural network for rendering high-order scattered radiance of participating media in real time. By reformulating the Radiative Transfer Equation (RTE) through theoretical examination, we propose **transmittance fields**, generated at a low cost, as auxiliary information to help the network better approximate the RTE, drastically reducing the size of the neural network. The light weight network efficiently estimates the difficult-to-solve in-scattering term and allows for configurable shading parameters while improving prediction accuracy. In addition, we propose a frequency-sensitive stencil design in order to handle non-cloud shapes, resulting in accurate shadow boundaries. Results show that our MRPNN is able to synthesize indistinguishable output compared to the ground truth. Most importantly, MRPNN achieves a speedup of two orders of magnitude compared to the state-of-the-art, and is able to render high-quality participating material in real time.
+
+[Paper](https://sites.cs.ucsb.edu/~lingqi/publications/paper_mrpnn.pdf) 
+
+[Video](coming soon) 
+
+[Suppl](coming soon) 
+
+[Project Page](coming soon)
+
+
+
+Recommended citation: 
+```
+coming soon
+```