ABSTRACTIn 3D reconstruction the stereo technique is one of the most used, generating point clouds of acceptable quality. One way to improve its quality is by fusing it with active systems such as lasers. For this fusion, a registration process can be used. It is important to evaluate the quality of the reconstruction in terms of spatial structure accuracy and visual appearance. A method of evaluating the quality of an active 3D stereo reconstruction is proposed, which takes a stereo camera image and compares it with a reprojected image of the registered cloud. The typical way of comparing two images calculates the point-to-point error, but does not take into account other aspects. Several quality metrics are studied to select the one that contributes the most in a given 3D reconstruction context. To improve the computational cost, a process of partitioning the images by regions is performed. A Quadtree partition was chosen with a homogeneity criterion that is selected among several criteria. The experiments and results for selecting the quality metric and the homogeneity criterion for the Quadtree partition are presented.KEYWORDS: 3D reconstructionstereo visionactive visionimage quality assessment (IQA)Quadtree partition Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsCamilo Chamorro-RiveraCamilo Chamorro-Rivera obtained the title of Engineer in Instrumentation and Control from the Colombian Polytechnic J.I.C. in 2002, where he has been a professor since 2008. He received a M.Sc. in Automation from U.P.B. of Medellín in 2009. His research areas include Process Automation and Computer Vision. He is currently a doctoral student in Electronics and Computing at University of Antioquia.Augusto Salazar-JimenezAugusto Salazar-Jimenez earned a Ph.D. in Automation in 2015. In 2007, he received his M.Sc. in Automation and a Bachelor in Electronics in 2004. All the degrees were received from National University of Colombia. His research interests cover Image Processing, Computer Vision and Pattern Recognition. His research is focused on the developing of tools using Deep Learning. Since 2014, he is professor at the Electronics Engineering of Universidad de Antioquia.