Skip to main content

Visual Regression

Visual regression testing is a critical aspect of ensuring the quality and consistency of 3D applications. While screenshot comparison is widely regarded as the informal industry standard, there are additional techniques and best practices that can enhance the effectiveness of visual regression testing. This article explores these techniques, strategies for managing baseline images, and the trade-offs between reliability and accuracy in screenshot comparison.

Managing and Updating Baseline Images​

Efficient management of baseline images is essential to accommodate design changes without introducing unnecessary overhead. Here are some best practices:

  1. Version Control: Use Git or another version control system to track changes to baseline images. This ensures traceability and allows for easy rollback if needed.
  2. Intentional Updates: Review baseline changes manually to ensure they are intentional and align with design updates.
  3. Consistent Naming and Structure: Adopt a clear naming convention and directory structure, such as including the test name, timestamp, and group name in the file path.
  4. Selective Updates: Update only the baseline images that correspond to actual design changes, avoiding blanket updates.
  5. Design Tokens: Link baseline images to specific design tokens or versions in tools like Figma to maintain consistency.
  6. Detailed Reports: Use tools that generate visual diff reports to highlight changes, making it easier to review and approve updates.

Reliability vs. Accuracy in Screenshot Comparison​

When using screenshot comparison, it is important to strike a balance between reliability and accuracy. Rendering differences, such as anti-aliasing or platform-specific variations, can introduce noise into the comparison. Here are some recommended parameters:

  • Pixel Match Threshold: 90-95% of pixels should match to consider the test reliable.
  • Color Tolerance: Allow a delta of 10-15 units in RGB values to account for minor rendering differences.
  • Ignored Regions: Define dynamic areas to exclude from comparison, such as temporary UI elements, animations, or post-processing effects.

Example in Playwright​

Below is an example of configuring screenshot comparison in Playwright:

const result = await compareScreenshots(image1, image2, {
  threshold: 0.9, // 90% of pixels must match
  colorDelta: 15, // Allow a color difference of 15 units in RGB
  ignoreRegions: [
    /* array of regions to ignore */
  ],
});

These parameters provide a good trade-off between catching meaningful regressions and avoiding false positives caused by insignificant differences.

Beyond Screenshot Comparison: Techniques for 3D Applications​

While screenshot comparison is the most common approach, other techniques can complement or enhance visual regression testing for 3D applications:

  1. Shader Validation: Test the correctness of shaders by comparing their outputs under controlled conditions.
  2. Geometry Validation: Verify the integrity of 3D models by comparing vertex positions, normals, and UV mappings.
  3. Lighting Consistency Checks: Ensure that lighting setups produce consistent results across different environments or rendering engines.
  4. Animation Snapshots: Capture keyframes of animations and compare them to ensure smooth transitions and correct poses.
  5. Depth Buffer Comparisons: Validate depth information to ensure proper rendering order and occlusion.

These techniques can be used in conjunction with screenshot comparison to provide a more comprehensive testing strategy.

By combining advanced techniques, efficient baseline management, and carefully tuned parameters, teams can ensure robust visual regression testing for 3D applications. This approach not only improves test reliability but also streamlines the process of accommodating design changes.