A Simulated Camera Pose Is Not Its Image Calibration: What to Record

A camera in a robot simulation may show a plausible scene while producing image measurements that do not match the intended physical camera. Before using synthetic images to test perception, separate the camera's image projection parameters from its position and orientation in the simulated scene. Both affect where an object appears in a picture, but they answer different questions and should be documented independently.

Describe the image as well as the camera pose

Record the simulated image width and height, camera model and the values used to describe its projection. The camera pose tells the system where the camera is and which way it faces relative to a reference frame. Intrinsic parameters describe the mapping from a point in camera coordinates to image coordinates under a particular camera model. A pose that looks reasonable in a 3D viewer does not prove the image projection is configured correctly.

OpenCV's calibration and 3D reconstruction interface documentation describes a pinhole model, an intrinsic matrix and the rotation and translation used to relate world and camera coordinates. That mathematical model is a starting point; a simulator's actual sensor model and software version must be checked in its own documentation.

Keep focal parameters and image size together

In the documented pinhole model, the intrinsic matrix includes focal parameters expressed in pixels and a principal point expressed in image coordinates. These values must be interpreted with the corresponding image dimensions. If an image is resized, a comparison that keeps old pixel-based parameters without accounting for the resize can produce misleading results. Cropping is a separate operation and needs its own coordinate treatment; do not assume that resizing and cropping have identical effects.

For a hypothetical experiment, an object is projected into a 640 by 480 image. A second image is displayed at half that width and height. The pixel coordinates in the second image cannot be compared directly to the first without accounting for the scaling. This example illustrates bookkeeping only; it does not claim a measurement from a real camera or a specific robot.

Distortion is a separate assumption

Real lenses can introduce distortion, and different camera models describe it with different parameters. OpenCV's camera calibration tutorial discusses lens distortion and calibration concepts. A synthetic sensor may use an ideal projection, a distortion model or additional image processing depending on its configuration. Record what the simulator actually implements instead of copying coefficients from an unrelated camera.

If the goal is to compare synthetic and physical images, identify the physical camera, lens, image resolution and calibration conditions. Keep the calibration data and its provenance with the experiment. Changing the lens, image processing or resolution can invalidate an earlier comparison. A visual resemblance between two pictures does not establish that calibration is correct across the field of view.

Design a controlled projection check

Use a scene with documented points or a known calibration target and save the exact scene and camera configuration. Record the reference frame, camera pose, image dimensions, projection parameters and any distortion or noise settings. Compare predicted and observed image locations using a method appropriate to the experiment. Change one documented parameter at a time and record the result; avoid tuning multiple fields until a picture merely looks right.

For a physical camera, calibration quality also depends on the captured views, target and processing method. A simulation can test whether an algorithm handles a chosen model, but it cannot prove the parameters of an unmeasured physical device. If an input value is estimated, label it as an estimate and explain how it was obtained.

When sharing a comparison with a colleague, include at least one example of the expected coordinate convention and the image orientation. An image that has been mirrored, rotated or cropped can appear plausible while the coordinates no longer correspond to the original projection. Record where those transformations occur in the pipeline and verify that the downstream software receives parameters for the image it actually processes. If the relevant convention is not documented, keep the comparison provisional and ask the sensor or software maintainer for clarification before drawing conclusions about the vision algorithm.

Preserve the link between data and configuration

A useful experiment bundle includes the image, sensor configuration, simulator and model versions, frame names, resolution, camera parameters and the code used for comparison. If images are rescaled or rectified, record the transformation and the parameters applicable to the resulting images. The next researcher should be able to identify which configuration produced each dataset, not infer it from a filename alone.