Abstract:In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude). The backbone of our approach is a combination of octree data partitioning, local Delaunay tetrahedralization and graph cut optimization. Graph cut optimization is used twice, once to extract surface hypotheses from local Delaunay tetrahedralizations and once to merge overlapping surfac… Show more
“…Malgré l'information essentielle contenue dans la texture, on remarque que de nombreux travaux de reconstruction 3D s'intéressent peu au problème et se limitent à assigner une intensité par primitive du modèle (sommet, facette, voxel, point 3D, surfel). Très souvent, pour représenter un modèle texturé, les travaux utilisent un maillage polygonal et se contentent d'assigner alors une intensité par facette ou sommet [Steinbruecker et al, 2014, Mostegel et al, 2017. Cette façon de procéder à le désavantage majeur de lier la résolution de la texture à celle de la géométrie du modèle reconstruit.…”
“…Malgré l'information essentielle contenue dans la texture, on remarque que de nombreux travaux de reconstruction 3D s'intéressent peu au problème et se limitent à assigner une intensité par primitive du modèle (sommet, facette, voxel, point 3D, surfel). Très souvent, pour représenter un modèle texturé, les travaux utilisent un maillage polygonal et se contentent d'assigner alors une intensité par facette ou sommet [Steinbruecker et al, 2014, Mostegel et al, 2017. Cette façon de procéder à le désavantage majeur de lier la résolution de la texture à celle de la géométrie du modèle reconstruit.…”
“…Unfortunately, existing algorithms often decouple the detection of local primitives from the construction of global structures. Continuing on our examples, object contouring methods typically detect line segments along image discontinuities before assembling them to form polygons [1], [2], and multiview stereo reconstruction algorithms extract 3D points by feature matching before interpolating them with a surface mesh [3], [4]. While this two-step approach reduces computational burden, the quality of the resulting structures depends heavily on the local decisions taken during primitive detection.…”
“…The mobility and maneuverability of UAVs to freely move in three dimensions and simultaneously capture close-up images of an object with arbitrary viewing angles allow to generate high-resolution and photo-realistic 3D models with high accuracy by processing a series of overlapping images with current state-of-the-art structure from motion (SfM) and multi-view stereo (MVS) pipelines, such as Pix4D [1], Bundler [2], or Colmap [3]. These models are of high interest in various fields, such as the use of digitized building models for 3D city modeling [4], object inspection [5], or cultural heritage documentation [6]. However, the quality of resulting 3D models strongly relies on flight plans that satisfy the requirements of an image-based 3D modeling process which include the acquisition of multiple overlapping images, sufficient baselines between the camera viewpoints and the prevention of optical occlusions from surrounding obstacles.…”
Small-scaled unmanned aerial vehicles (UAVs) emerge as ideal image acquisition platforms due to their high maneuverability even in complex and tightly built environments. The acquired images can be utilized to generate high-quality 3D models using current multi-view stereo approaches. However, the quality of the resulting 3D model highly depends on the preceding flight plan which still requires human expert knowledge, especially in complex urban and hazardous environments. In terms of safe flight plans, practical considerations often define prohibited and restricted airspaces to be accessed with the vehicle. We propose a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace. The generated trajectories account for the desired model resolution and the demands on a successful photogrammetric reconstruction. We exploit semantics from an initial flight to extract the target object and to define restricted and prohibited airspaces which have to be avoided during the path planning process to ensure a safe and short UAV path, while still aiming to maximize the object reconstruction quality. The path planning problem is formulated as an orienteering problem and solved via discrete optimization exploiting submodularity and photogrammetrical relevant heuristics. An evaluation of our method on a customized synthetic scene and on outdoor experiments suggests the real-world capability of our methodology by providing feasible, short and safe flight plans for the generation of detailed 3D reconstruction models.
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