Lightweight Distance Estimation from Single Images without Camera Intrinsics

In this paper, we address the problem of estimating the distance between a camera and a photographed object using minimal prior information. Specifically, the goal is to obtain distance estimates without access to intrinsic camera parameters such as focal length, sensor size, or lens distortion coefficients. We propose three simple heuristic methods, which can be viewed as lightweight variants of the classical camera pinhole model (CPH), but do not require calibration. Instead, they rely only on the approximate real height of a known object in the scene. The methods were validated on a representative dataset of images captured with several modern cameras and smartphones, using known object dimensions as ground truth. Their performance was compared against CPH using error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), mean signed error (MSD), coefficient of determination R2), and supported by statistical testing (Shapiro–Wilk, ANOVA, Kruskal–Wallis). The analysis confirmed that, while less precise than fully calibrated approaches, the proposed heuristics achieve consistent and reliable distance estimates under minimal assumptions. These methods are particularly suited for lightweight applications and devices with fixed focal lengths, such as smartphones.