Street View Motion-from-Structure-from-Motion

Klingner, Bryan M.; Martín, David; Roseborough, James B.

doi:10.1109/iccv.2013.122

Cited by 98 publications

(72 citation statements)

References 24 publications

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“…Recent advances in computer vision include SfM algorithms, which have been successfully used for the reconstruction of large uncontrolled photo collections [24][25][26]. Studies have described the adaptation of SfM techniques to process UAV data photography to create an accurate georeferenced 3D point cloud and to generate a high-resolution DSM; SfM techniques have also been used in the orthorectification of georeferenced images that have been joined together to form a mosaic [6,19].…”

Section: Introductionmentioning

confidence: 99%

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Παπακωνσταντίνου

Topouzelis

Pavlogeorgatos

2016

IJGI

112

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Abstract:Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth's surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.

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Section: Introductionmentioning

confidence: 99%

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Παπακωνσταντίνου

Topouzelis

Pavlogeorgatos

2016

IJGI

112

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“…obtained from dominant scene planes [8] in a prior sparse reconstruction step like [13] or [16]) and a single sweeping direction. The sweep is performed by creating additional planes within the distance range [D min , D max ].…”

Section: Discussionmentioning

confidence: 99%

“…In a later work, Hedborg et al [13] have shown a full bundle adjustment including motion effects as well. Most recently Klingner et al [16] proposed a structure from motion pipeline, for cameras mounted on a car, which uses relative pose prior along the vehicle path.…”

Section: Previous Workmentioning

confidence: 99%

Rolling Shutter Stereo

Saurer

Köser

Bouguet

et al. 2013

2013 IEEE International Conference on Computer Vision

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A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.

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“…Some of the largest sources of pictures online are street view imagery (SVI) services, which offer 360 • panoramas from many roads, streets and other places around the world (Anguelov et al, 2013). They allow remote observation of areas without physical access to them, thus remaining cost-effective, with applications in navigation, tourism, building texturing, image localisation, point clouds georegistration and motion from structure from motion (Zamir et al, 2010;Anguelov et al, 2010;Klingner et al, 2013;Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Using street view imagery for 3-D survey of rock slope failures

Voumard

Abellán

Nicolet

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. We discuss here different challenges and limitations of surveying rock slope failures using 3-D reconstruction from image sets acquired from street view imagery (SVI). We show how rock slope surveying can be performed using two or more image sets using online imagery with photographs from the same site but acquired at different instances. Three sites in the French alps were selected as pilot study areas: (1) a cliff beside a road where a protective wall collapsed, consisting of two image sets (60 and 50 images in each set) captured within a 6-year time frame; (2) a large-scale active landslide located on a slope at 250 m from the road, using seven image sets (50 to 80 images per set) from five different time periods with three image sets for one period; (3) a cliff over a tunnel which has collapsed, using two image sets captured in a 4-year time frame. The analysis include the use of different structure from motion (SfM) programs and a comparison between the extracted photogrammetric point clouds and a lidar-derived mesh that was used as a ground truth. Results show that both landslide deformation and estimation of fallen volumes were clearly identified in the different point clouds. Results are site-and softwaredependent, as a function of the image set and number of images, with model accuracies ranging between 0.2 and 3.8 m in the best and worst scenario, respectively. Although some limitations derived from the generation of 3-D models from SVI were observed, this approach allowed us to obtain preliminary 3-D models of an area without on-field images, allowing extraction of the pre-failure topography that would not be available otherwise.

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Street View Motion-from-Structure-from-Motion

Abstract: We describe a structure- from-motion

Cited by 98 publications

References 24 publications

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Rolling Shutter Stereo

Using street view imagery for 3-D survey of rock slope failures

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