Papers for

photogrammetry software teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Image editors reveal hidden camera settings by painting checkerboards

The Camera Inside the Editor: Reading the Implicit Camera of Image Editors with Painted Calibration Patterns

Abstract: Instruction-based image editors insert objects, restyle scenes and render new viewpoints, but it is unknown which camera they assume when they paint into a photograph. Asked to cover the floor with a checkerboard, an editor paints projective structure from which classical vanishing-point geometry reads pitch, roll, focal length, yaw and, on renders, the principal point, without any training. Unlike a calibrator such as GeoCalib, which estimates the camera of an image, this isolates the camera under which the editor paints. On 120 rendered cameras with exact ground truth, Qwen-Image-Edit-2511 paints tile edges that meet their vanishing points within 0.26 degrees, and its implicit camera matches the true one to 0.8 degrees in pitch and 6% in focal length, more accurately than GeoCalib except in roll. Asked to draw the horizon or mark a vanishing point instead, the editor fails, so this knowledge is revealed by painting and not by the explicit tasks we tried. The implicit camera has two priors: roll is pulled towards level (slope 0.71), and telephoto perspective towards a default of about 30 mm, which roughly matches the camera the models paint without any scene. For Qwen, the priors do not grow when blur removes four fifths of the line evidence. They are stronger on real photographs, and on NYUv2 a shorter wording of the task removes the difference for roll. On photographs from a 24--240 mm zoom lens the painted perspective grows with only 0.62 of the lens's slope, while GeoCalib and MoGe-2 saturate at about 52 and 42 mm. FLUX.1 Kontext and LongCat-Image-Edit are pulled much harder. Finally, from a level camera a camera-control LoRA executes pose commands at only 50--70% of their strength, and a board painted into its output agrees with the camera it produced.

Tue 29 SeptComputer Vision and Pattern RecognitionMachine Learning
The gist
Many image editing tools change pictures by adding or moving objects but don’t show what camera view they assume when doing so. The authors found that when these editors are asked to fill an area with a checkerboard pattern, the way they draw the pattern reveals the camera settings they implicitly use, like angle and zoom. They tested this by comparing the editor’s guessed camera with the true camera and found close matches. Interestingly, these hidden camera clues only appear when the editor paints patterns, not when asked directly about the camera view.
Open → 2609.37732v1