Technical reports
Neural representation of Bidirectional Reflectance Distribution Function
March 2026, 73 pages
This technical report is based on a dissertation submitted May 2025 by the author for the degree of Bachelor of Arts (Computer Science Tripos) to the University of Cambridge, Magdalene College.
The author was supervised by Dr Chenliang Zhou and Dr Alejandro Sztrajman.
| DOI | https://doi.org/10.48456/tr-1005 |
Abstract
Despite the advent of neural rendering, specifically supervised models trained on Bidirectional Reflectance Distribution Function (BRDF) for appearance modelling, there is limited understanding of their effectiveness, efficiency, and utility for downstream research.
In this work, I designed implicit neural representations of BRDFs and evaluated their real-world performance relative to classical models, with extensions to sparse-sample reconstruction and multi-modal material synthesis. I further investigated importance sampling strategies in the rendering pipeline, alongside both supervised and generative methods.
Building upon these contributions, I implemented a novel multi-modal generative pipeline and proposed new quantitative metrics for material synthesis, addressing a long-standing gap in the evaluation of neural materials.
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BibTeX record
@TechReport{UCAM-CL-TR-1005,
author = {Hu, Zheyuan},
title = {{Neural representation of Bidirectional Reflectance
Distribution Function}},
year = 2026,
month = mar,
url = {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-1005.pdf},
institution = {University of Cambridge, Computer Laboratory},
doi = {10.48456/tr-1005},
number = {UCAM-CL-TR-1005}
}