Non-parametric modeling and editing for captured SVBRDF

• We design a device for capturing SVBRDF data and a series of calibration algorithms.
• We propose a new interpolation algorithm based on solving Laplace equations with Neumann boundary condition on the 2D concentric squares map for a 2D slice BRDF data at a single view.
• We propose an efficient SVBRDF reconstruction algorithm based on Kernel Nyström from sparse spatial BRDF samples, which only requires computing with matrix multiplication.
• We propose a fast and practical basis BRDFs extraction method based on iterative clustering and linear least squares optimization.

Reflectance properties of real-world objects can be represented by spatially varying bidirectional reflectance distribution function (SVBRDF). Non-parametric BRDF becomes the main aspect nowadays because of its verisimilitude and generality. We present a new method for capturing, modeling and editing non-parametric SVBRDF. Our method seeks to achieve high reconstruction accuracy, compactness and editability of representation, and meanwhile to speed up the SVBRDF modeling processes. For a planar surface, we (1) design a capturing device to acquire reflectance samples of the surface; (2) propose a Laplacian-based angular interpolation scheme for a 2D slice of BRDF at a given surface location, and a kernel Nyström method for SVBRDF data matrix reconstruction; (3) propose a practical algorithm to extract linearly independent basis BRDFs, and to calculate blending weights by projecting reconstructed reflectance onto these basis BRDFs; (4) decompose these basis BRDFs into groups of 1D curves for editing intuitively. Our experiment results demonstrate that our approach can model real-world reflectance with both high accuracy and high visual fidelity for real-time virtual environment rendering.

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