The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-fromMotion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.

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International audienceAutomatic image-based-modeling usually has two steps: Structure from Motion (SfM) and the estimation of a trian-gulated surface. The former provides camera poses and a sparse point cloud. The latter usually involves dense stereo. From the computational standpoint, it would be nice to avoid dense stereo and estimate the surface from the sparse cloud directly. Furthermore, it would be useful for online applications to update the surface while the camera is moving in the scene. This paper deals with both requirements: it introduces an incremental method which reconstructs a surface from a sparse cloud estimated by incremental SfM. The context is new and difficult since we ensure the resulting surface to be manifold at all times. The manifold property is important since it is needed by differential operators involved in surface refinements. We have experimented with a hand-held omnidirectional camera moving in a city

The majority of methods for the automatic surface reconstruction of a scene from an image sequence have two steps: Structure-from-Motion and dense stereo. From the complexity viewpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse features reconstructed by SfM. This paper adds two contributions to our previous work on 2-manifold surface reconstruction from a sparse SfM point cloud: we quantitatively evaluate our results on standard multiview dataset and we integrate the reconstruction of image curves in the process.