Efficient Moving Point Handling for Incremental 3D Manifold Reconstruction

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Matteucci

2015

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Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping. When computational power represents a limited resource and, a detailed map is not the primary goal, the reconstruction can be performed incrementally, from a monocular video, carving a 3D Delaunay triangulation of sparse points; this allows online incremental mapping for tasks such as traversability analysis or obstacle avoidance. To exploit the sharp edges of urban landscape, we propose to use a Delaunay triangulation of Edge-Points, which are the 3D points corresponding to image edges. These points constrain the edges of the 3D Delaunay triangulation to real-world edges. Besides the use of the Edge-Points, a second contribution of this paper is the Inverse Cone Heuristic that preemptively avoids the creation of artifacts in the reconstructed manifold surface. We force the reconstruction of a manifold surface since it makes it possible to apply computer graphics or photometric refinement algorithms to the output mesh. We evaluated our approach on four real sequences of the public available KITTI dataset by comparing the incremental reconstruction against Velodyne measurements.

Section: Discussionmentioning

confidence: 99%

Incremental reconstruction of urban environments by Edge-Points Delaunay triangulation

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Matteucci

2015

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“…The authors in [1] and [2] do not manage moving points, however, as new images are processed, the point positions estimates are updated by the SLAM algorithm. Only in [18] a simple heuristic to manage moving points has been proposed, but it approximates the visibility updates induced by the moving points.…”

Section: E Ray Tracing Schedulermentioning

confidence: 99%

Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction

Piazza

IEEE Robot. Autom. Lett.

Matteucci

2018

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In Robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of GPU computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real-time. In this paper we improved and extended the state of the art incremental manifold mesh algorithm proposed in [1] and extended in [2]. While these algorithms do not achieve real-time and they embed points from SLAM or Structure from Motion only when their position is fixed, in this paper we propose the first incremental algorithm able to reconstruct a manifold mesh in real-time through single core CPU processing which is also able to modify the mesh according to 3D points updates from the underlying SLAM algorithm. We tested our algorithm against two state of the art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.

“…We reconstruct a first manifold with the algorithm proposed in [17], slightly modified by a weighting scheme as in [22,21], bootstrapping from the camera poses, the reconstructed points and the visibility estimated by a Structure from Motion algorithm 1 . The manifold is reconstructed such that it partitions the 3D triangulation of the SfM points between the set O of outside tetrahedra, i.e., the subset of the free space outside the manifold (not all the free space tetrahedra will be part of the space outside the manifold), and the complementary set I of inside tetrahedra (i.e., the remaining tetrahedra that represent the matter together with the free space tetrahedra which would invalidate the manifold property).…”

Section: Manifold Reconstructionmentioning

confidence: 99%

“…In the literature, some methods to estimate a manifold mesh exist, but most of them rely on silhouettes [31,6,5], thus they are limited to small objects, or they are not scalable due to the usage of voxel-based reconstruction [10]. A suitable approach to estimate a manifold mesh has been recently proposed in [15,17,21,22]: the authors incrementally reconstruct the scene from very sparse data which are the outcome of SfM algorithms. For many applications the reconstruction relying only on these points is sufficient, but for a surface evolution approach, it needs resolution and accuracy improvements.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D reconstruction of manifold meshes via delaunay triangulation and mesh sweeping

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

Delaunoy

Pollefeys

et al. 2016

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In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images. More precisely we focus on the automatic initialization of a 3D mesh as close as possible to the final solution; indeed many approaches require a good initial solution for further refinement via multi-view stereo techniques. Our novel algorithm automatically estimates an initial manifold mesh for surface evolving multi-view stereo algorithms, where the manifold property needs to be enforced. It bootstraps from 3D points extracted via Structure from Motion, then iterates between a state-of-the-art manifold reconstruction step and a novel mesh sweeping algorithm that looks for new 3D points in the neighborhood of the reconstructed manifold to be added in the manifold reconstruction. The experimental results show quantitatively that the mesh sweeping improves the resolution and the accuracy of the manifold reconstruction, allowing a better convergence of state-of-the-art surface evolution multi-view stereo algorithms.