Wen Cao
I am currently pursuing a Ph.D. @Computer Graphics and Image Processing,
Linköping University, Sweden, as a Marie Curie Fellow (ESR) under the PRIME-ITN,
supervised by Prof. Jonas Unger.
Specific focus of my research is on the development of material capture and model from real world objects and scenes, Computer Graphics, Machine Learning, DL.
Publications
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- website
- arXiv
- Presenter
- code
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abstract
This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectral coded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor. We propose a model that employs compressed sensing techniques to reconstruct the complete multi-spectral light field from undersampled measurements. Unlike previous work where a light field is vectorized to a 1D signal, our method employs a 5D basis and a novel 5D measurement model, hence, matching the intrinsic dimensionality of multispectral light fields. We mathematically and empirically show the equivalence of 5D and 1D sensing models, and most importantly that the 5D framework achieves orders of magnitude faster reconstruction while requiring a small fraction of the memory. Moreover, our new multidimensional sensing model opens new research directions for designing efficient visual data acquisition algorithms and hardware.
- paper
- website
- code
- Poster
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abstract
Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in realistic image rendering. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel iterative method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. The method is a two step approach, where the first step takes an image of the physical material as input and uses a lightweight neural network to estimate the parameters of an analytic BRDF model. The second step adaptive sample the measurements using the estimated BRDF model and an image loss to maximize the BRDF representation accuracy. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representatio.
Multidimensional Compressed Sensing for Spectral Light Field Imaging
Wen Cao,Ehsan Miandji, Jonas Unger
VISAPP 2024
"a compressive multi-spectral light field camera model that utilizes a one-hot spectral coded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor."
Wen Cao,Ehsan Miandji, Jonas Unger
VISAPP 2024
"a compressive multi-spectral light field camera model that utilizes a one-hot spectral coded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor."
Deep image-based Adaptive BRDF Measure
Wen Cao
GRAPP 2025
"presents a novel iterative method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setups"
Wen Cao
GRAPP 2025
"presents a novel iterative method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setups"