Abstract In this paper, we propose a new method for reconstructing 3D models from a noisy and incomplete 3D scan and a coarse template model. The main idea is to maintain characteristic high‐level features of the template that remain unchanged for different variants of the same type of object. As invariants, we chose the partial symmetry structure of the template model under Euclidian transformations, i.e. we maintain the algebraic structure of all reflections, rotations and translations that map the object partially to itself. We propose an optimization scheme that maintains continuous and discrete symmetry properties of this kind while registering a template against scan data using a deformable iterative closest points (ICP) framework with thin‐plate‐spline regularization. We apply our new deformation approach to a large number of example data sets and demonstrate that symmetry‐guided template matching often yields much more plausible reconstructions than previous variants of ICP.

Given a set of 2D images, we propose a novel approach for the reconstruction of straight 3D line segments that represent the underlying geometry of static 3D objects in the scene. Such an algorithm is especially useful for the automatic 3D reconstruction of man-made environments. The main contribution of our approach is the generation of an improved reconstruction by imposing global topological constraints given by connections between neighbouring lines. Additionally, our approach does not employ explicit line matching between views, thus making it more robust against image noise and partial occlusion. Furthermore, we suggest a technique to merge independent reconstructions, that are generated from different base images, which also helps to remove outliers. The proposed algorithm is evaluated on synthetic and real scenes by comparison with ground truth.