siatheindochinese/LUT_to_K

Recover a camera intrinsic matrix (K) from a corresponding lookup table (LUT) through numerical optimization.

LUT_to_K

Recover a camera intrinsic matrix (K) from a corresponding lookup table (LUT) through numerical optimization.

To-Do List

• Load LUT from .bin files
• Generate LUT from K
• Generate pixel-ray correspondences
• Formulate problem statement
• Implement naive solution
• LUT sanity check
• Random K generator
• Implement numerical solutions
• Optional: recover distortion coefficients

1 Problem Statement

Certain AR/MR devices do not provide intrinsic values directly for their cameras. Instead, high-level functions exist to project 3D points to their corresponding 2D pixels and backproject 2D pixels to their corresponding rays (e.g. HoloLens2 Research Mode API has MapImagePointToCameraUnitPlane and MapImagePointToCameraUnitPlane).

Most popular computer vision libraries (e.g. OpenCV) require a 3x3 intrinsic matrix for their functions. Rewriting these functions to accomodate the high-level 2D-3D mapping functions from the aforementioned devices is an extremely tedious task.

We intend to recover these intrinsic parameters by sampling pixel-ray correspondences obtained from these high-level functions and use them to solve for the 4 intrinsic parameters $f_x$, $f_y$, $c_x$ and $c_y$.

Ultimately, this problem can be formulated as two systems of linear equations $Ax = b$ as follows:

$$ u_{i} = \begin{bmatrix} \frac{X_{i}}{Z_{i}} & 1 \end{bmatrix} \begin{bmatrix} f_{x} \\ c_{x} \end{bmatrix} \text{ and } v_{i} = \begin{bmatrix} \frac{Y_{i}}{Z_{i}} & 1 \end{bmatrix} \begin{bmatrix} f_{y} \\ c_{y} \end{bmatrix} \text{ for } i = 1, \cdots,wh $$

where $[u_{i}, v_{i}]^T$ are pixel coordinates, $[X_{i}, Y_{i}, Z_{i}]^T$ are the corresponding rays and $w$ and $h$ are the width and height of the LUT.

In theory, only 2 pixel-ray correspondences are needed to solve for all 4 intrinsic parameters. We may choose to use all pixel-ray correspondences from a LUT, resulting in two overdetermined systems of linear equations.

$$ \begin{bmatrix} u_{1} \\ u_{2} \\ \vdots \\ u_{n} \end{bmatrix} = \begin{bmatrix} \frac{X_{1}}{Z_{1}} & 1 \\ \frac{X_{2}}{Z_{2}} & 1 \\ \vdots \\ \frac{X_{n}}{Z_{n}} & 1 \end{bmatrix} \begin{bmatrix} f_{x} \\ c_{x} \end{bmatrix} \text{ and } \begin{bmatrix} v_{1} \\ v_{2} \\ \vdots \\ v_{n} \end{bmatrix} = \begin{bmatrix} \frac{Y_{1}}{Z_{1}} & 1 \\ \frac{Y_{2}}{Z_{2}} & 1 \\ \vdots \\ \frac{Y_{n}}{Z_{n}} & 1 \end{bmatrix} \begin{bmatrix} f_{y} \\ c_{y} \end{bmatrix} $$

The first system of linear equations can be solved to obtain $f_{x}$ and $c_{x}$. Likewise, $f_{y}$ and $c_{y}$ can be obtained by solving the second system of linear equations.

2 Solutions (to-be-added)

Methods to solve for the intrinsic parameters can be categorized into either direct computation, or numerical optimization.

To use the solvers, your LUT must be a numpy array $lut$ with shape $(h,w,3)$, where indexing $lut[i,j]$ provides the corresponding ray $[X,Y,Z]$. An example using a least-squares solver is shown below.

from utils import pixAndRay
from solver import leastSquares

coords_2d, rays_3d = pixAndRay(lut)
K_approx = leastSquares(coords_2d, rays_3d)

A demo_lstsq.py script is provided that solves 4 different LUTs using direct least-squares computation.

2.1 Least-Squares solution (direct computation)

Given an overdetermined system of linear equations $Ax = b$, we can obtain the least-squares solution $\hat{x} = (A^TA)^{-1}A^Tb$ through direct computation.