The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3D traffic signs from their 2D detections to demonstrate its capacity to solve ambiguities.
Vallet, B., Soheilian, B., & Brédif, M. (2014). Combinatorial clustering and Its Application to 3D Polygonal Traffic Sign Reconstruction From Multiple Images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II–3, 165–172. https://doi.org/10.5194/isprsannals-ii-3-165-2014