3D Data Accuracy Notes¶
When you create charts in Blender’s 3D environment, you step beyond traditional 2D plotting. This opens up powerful new ways to explore and present data, but it also means that charts may not always represent values with perfect accuracy. The following notes explain the kinds of distortions you may encounter, so you can use them intentionally and understand their impact.
Geometric & Spatial Factors¶
Perspective distortion: Objects closer to the camera appear larger, which can shift alignment with axes.
Camera angle & focal length: Wide‑angle lenses exaggerate depth, while telephoto lenses flatten space, both changing how values appear.
Projection type: Orthographic views preserve parallelism, while perspective views converge lines, potentially skewing scale.
Depth occlusion: Data points may overlap or hide behind others.
Axis skewing: Rotating charts in 3D space can make axes appear misaligned or non‑uniform.
Numerical & Precision Factors¶
Floating‑point precision limits: Blender’s internal decimal accuracy can cause rounding errors in very large or very small datasets.
Scaling artifacts: Extremely large or tiny objects may lose detail or collapse due to precision loss.
Unit conversions: Blender’s default unit system (meters) may rescale imported data unexpectedly.
Negative values: Some charts support negative values with tweaking, but mostly positive values must be used in datasets.
Visual & Rendering Factors¶
Lighting & shading: Shadows can exaggerate or diminish perceived size or depth.
Material thickness: Extruded meshes (bars, lines) have volume, which can visually inflate values compared to flat 2D lines.
Transparency & overlap: Semi‑transparent objects can blend visually, making distinctions harder.
Anti‑aliasing & resolution: At certain scales, edges blur, reducing clarity of fine differences.
Cognitive & Perceptual Factors¶
Depth perception bias: Human vision is less accurate at judging distances in depth than in flat 2D space.
Foreshortening: Objects angled away from the viewer appear shorter, even if their actual data value is unchanged.
Relative comparison difficulty: Comparing values across different axes in 3D is harder than scanning a flat 2D grid.
Chart clutter: Extra dimensions (depth, volume, materials) can overwhelm, reducing clarity instead of adding insight.
Workflow & Technical Factors¶
Animation interpolation: Keyframe curves may smooth or overshoot values, distorting raw data representation.
Modifiers & constraints: Geometry Nodes or modifiers can unintentionally alter scale or position.
Export differences: Rendering to 2D (image/video) flattens the 3D chart, which may reintroduce distortions depending on camera projection.
Important
This addon is designed for visual exploration and communication of data, not for exact analytical measurement.
Charts created with this addon may introduce visual distortions due to the factors listed above.
These distortions can be useful for highlighting trends or creating engaging presentations, but they mean the visualization is not guaranteed to be 100% accurate.
If precise, publication‑ready accuracy is required, always verify your data with traditional 2D charting tools or statistical software before publishing.