Structure from Motion (SFM) – Uma Breve Revisão Histórica, Aplicações nas Geociências e Perspectivas Futuras

O presente artigo de revisão narra a trajetória de um dos algoritmos mais utilizados na fotogrametria com Veículo Aéreo não Tripulado (VANT), que a partir da visão computacional, tem se destacado como solução de baixo custo para obtenção de informações da superfície terrestre. Apesar de sua concepção ter sido formulada em meados da década de 1950 e com propósitos distantes das geociências, foi a partir dos avanços da indústria da computação e robótica, no início da década de 1980, que o Structure from Motion (SfM) absorveu significativas melhorias para consagrá-lo como um importante recurso de modelagem tridimensional. No entanto, somente na última década (2010), observou-se um exponencial crescimento nas aplicações e análises do SfM nas geociências, principalmente a partir da popularização dos VANTs. Com isso, vieram à tona suas principais aplicações e limitações – neste estudo também serão abordadas suas características, principalmente as que diferem de técnicas já consagradas como o LiDAR, e perspectivas futuras dessa tecnologia

Automatic registration of MLS point clouds and SfM meshes of urban area

Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently. Currently, there are various methods for acquiring large-scale 3D scan data, such as Mobile Laser Scanning (MLS), Airborne Laser Scanning, Terrestrial Laser Scanning, photogrammetry and Structure from Motion (SfM). Especially, MLS is useful to acquire dense point clouds of road and road-side objects, and SfM is a powerful technique to reconstruct meshes with textures from a set of digital images. In this research, a registration method of point clouds from vehicle-based MLS (MLS point cloud), and textured meshes from the SfM of aerial photographs (SfM mesh), is proposed for creating high-quality surface models of urban areas by combining them. In general, SfM mesh has non-scale information; therefore, scale, position, and orientation of the SfM mesh are adjusted in the registration process. In our method, first, 2D feature points are extracted from both SfM mesh and MLS point cloud. This process consists of ground-and building-plane extraction by region growing, random sample consensus and least square method, vertical edge extraction by detecting intersections between the planes, and feature point extraction by intersection tests between the ground plane and the edges. Then, the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently, using similarity invariant features and hashing. Next, the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted. Finally, scaling Iterative Closest Point algorithm is applied for accurate registration. Experimental results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings

Automatic registration of MLS point clouds and SfM meshes of urban area

Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently. Currently, there are various methods for acquiring large-scale 3D scan data, such as Mobile Laser Scanning (MLS), Airborne Laser Scanning, Terrestrial Laser Scanning, photogrammetry and Structure from Motion (SfM). Especially, MLS is useful to acquire dense point clouds of road and road-side objects, and SfM is a powerful technique to reconstruct meshes with textures from a set of digital images. In this research, a registration method of point clouds from vehicle-based MLS (MLS point cloud), and textured meshes from the SfM of aerial photographs (SfM mesh), is proposed for creating high-quality surface models of urban areas by combining them. In general, SfM mesh has non-scale information; therefore, scale, position, and orientation of the SfM mesh are adjusted in the registration process. In our method, first, 2D feature points are extracted from both SfM mesh and MLS point cloud. This process consists of ground-and building-plane extraction by region growing, random sample consensus and least square method, vertical edge extraction by detecting intersections between the planes, and feature point extraction by intersection tests between the ground plane and the edges. Then, the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently, using similarity invariant features and hashing. Next, the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted. Finally, scaling Iterative Closest Point algorithm is applied for accurate registration. Experimental results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings