Probabilistic Fusion of Depth Maps with Local Variance Estimation

Henry Schaub, Nico Leuze, Maximilian Hoh, Alfred Schöttl

Abstract: Depth sensors in general, but especially consumer level sensors, show significantly varying noise characteristics depending on the environment and the measured material. If only an empirically determined sensor model is used, the quality of some reconstructed surface areas might therefore be worse than estimated. In order to gain an accurate surface approximation with meaningful corresponding estimation variances, noise characteristics of surface elements must be treated individually. We propose a probabilistic approach that combines an empirically determined sensor noise model with an incrementally updated estimation of the local noise distribution. Experiments on a publicly available dataset demonstrate that our algorithm reconstructs challenging scenes with comparatively high accuracy and fewer outliers.

External IDs:dblp:conf/ieeesensors/SchaubLHS23