Digital documentation for the conservation of 2.5D painted surfaces requires high-resolution data. Geometric detail information can provide insight into the brushwork or condition of a painting, can be used for monitoring, to create facsimiles, or for valorization purposes. The issue with geometric documentation lies in its democratization; commercial systems are expensive. This thesis explores the use of data fusion to achieve high-resolution detail documentation (less than 100 um lateral spatial resolution) at a low-cost. The data fusion combines two image-based methods: photogrammetry and photometric stereo. An optimal result can be achieved by fusing the relevant frequencies from both methods. A case study comparing photogrammetry, photometric stereo, data fusion, and data from a triangulation-based scanner showed that the data fusion improves the results of the photogrammetry and photometric stereo but is inferior when compared to the triangulation-based scanner. Data fusion is a good low-cost alternative to expensive commercial methods.