Planar Intrinsics with Real Data

[COLLAB] This chapter requires user collaboration for dataset-specific details, data collection advice, and practical tips.

This chapter walks through the planar_real example, which calibrates a camera from real chessboard images.

Dataset

The example uses images from data/stereo/imgs/leftcamera/:

• Pattern: 7×11 chessboard, 30 mm square size
• Images: Multiple PNG files (Im_L_*.png)
• Camera: Standard industrial camera (specific model TBD)

Workflow

1. Corner Detection

The example uses the chess-corners crate for chessboard detection:

#![allow(unused)]
fn main() {
use chess_corners::{ChessboardParams, ChessConfig, detect_chessboard};

let board_params = ChessboardParams {
    rows: 7,
    cols: 11,
    square_size: 0.03, // 30 mm
};
}

Each image is processed independently. Failed detections are skipped — the workflow continues with the views that succeed.

2. Dataset Construction

Detected corners are assembled into CorrespondenceView structures with matched 3D board points:

#![allow(unused)]
fn main() {
let views: Vec<View<NoMeta>> = images
    .iter()
    .filter_map(|img| {
        let corners = detect_chessboard(img, &config)?;
        let obs = CorrespondenceView::new(board_3d.clone(), corners);
        Some(View::without_meta(obs))
    })
    .collect();
let dataset = PlanarDataset::new(views)?;
}

3. Calibration

The pipeline is identical to the synthetic case:

#![allow(unused)]
fn main() {
let mut session = CalibrationSession::<PlanarIntrinsicsProblem>::new();
session.set_input(dataset)?;
step_init(&mut session, None)?;
step_optimize(&mut session, None)?;
let export = session.export()?;
}

Interpreting Results

Key outputs to examine:

• Focal lengths ($f_x$, $f_y$): Should match the expected value based on sensor size and lens focal length. For a 1/2" sensor with 8mm lens: $f\approx 800$ pixels.
• Principal point ($c_x$, $c_y$): Should be near the image center. Large offsets may indicate a decentered lens.
• Distortion ($k_1$, $k_2$): Negative $k_1$ indicates barrel distortion (common). $|k_1|>0.3$ suggests a wide-angle lens.
• Reprojection error: <1 px is good. >2 px suggests problems with corner detection or insufficient view diversity.

Comparison with Synthetic Data

Practical Tips

• Use 15-30 images with diverse viewpoints
• Cover the full image area — points only near the center poorly constrain distortion
• Include tilted views — not just frontal. Rotation around both axes constrains all intrinsic parameters
• Check corner ordering — mismatched 2D-3D correspondences cause calibration failure
• Start with fix_k3: true — only estimate $k_3$ if reprojection error is high and you suspect higher-order radial distortion

Running the Example

cargo run -p vision-calibration --example planar_real

The example prints per-step results including initialization accuracy, optimization convergence, and final calibrated parameters.