Structure From Motion Approach Research Articles

UAV Photogrammetric Surveys for Tree Height Estimation

In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.

AirSUM - Structure from Motion supported stock unearthing Method: Erosion modeling in Viennese vineyards

Soil mobilization is particularly high in viticulture. In Austria and especially in Vienna, soil protection measures are frequently implemented without the vintners knowing how high their erosion rates are and where in particular they can introduce additional measures. New, highly accurate and cost-efficient methods for erosion estimation are expected to improve this situation.A relatively fast and low-cost option is the stock unearthing method (SUM), which provides a rough estimation of erosion based on biomarkers, but neglects the inter-row area. The improved stock unearthing method (ISUM) approach uses additional measurement points in this area and therefore delivers more accurate erosion volumes. Additionally, the use of Structure from Motion (SfM) DEMs provides respectable results on small plots in vineyards. This study combines and evaluates the unmanned aerial vehicle (UAV) SfM approach with the SUM. This results in a millimeter scale resolution and can be applied to larger study areas. The combination of SUM and SfM forms the new airSUM approach that provides a more accurate representation of the recent relief, rendering interpolation techniques unnecessary, reproducing visible runoff patterns, and enhancing erosion volume estimations. The use of airSUM enabled the detection of 32.7 ± 17.5 m3 (avg. of ∼ 84.1 ± 45 t ha−1 yr−1) soil erosion on an area of 700 m2 in a period of 8 years. Erosion hotspots could be modeled mainly in the wheel tracks with depths of up to 20.5 cm parallel to the slope and correspond excellently with field observations. This is partly due to the compaction of the surface, but mainly due to the preferential runoff and erosion. The identification of the erosion hot spots, runoff breaches and consequent runoff concentration allows the precise allocation of mitigation funds to reduce overland flow and erosion.

Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

Expeditious Low-Cost SfM Photogrammetry and a TLS Survey for the Structural Analysis of Illasi Castle (Italy)

The structural analysis of degraded historical buildings requires an adequate 3D model of the object. Structure from motion (SfM) photogrammetry and laser scanning geomatics techniques can satisfy this request by providing geometrically affordable data. The accuracy and resolution depend on the instruments and procedures used to extract the 3D models. This work focused on a 3D survey of Illasi Castle, a strongly degraded historical building located in northern Italy, aimed at structural analysis in the prevision of a static recovery. A low-cost drone, a single-lens reflex (SLR) camera, and a smartphone were used in the survey. From each acquired dataset, using the integration between the images acquired by the drone and the SLR camera, a 3D model of the building was extracted by means of the SfM technique. The data were compared with high-precision and high-resolution terrestrial laser scanning (TLS) acquisitions to evaluate the accuracy and performance of the fast and low-cost SfM approach. The results showed a standard deviation value for the point cloud comparisons in the order of 2–3 cm for the best solution (integrating drone and SLR images) and 4–7 cm using smartphone images. Finally, the integration of the best SfM model of the external walls and the TLS model of the internal portion of the building was used in finite element (FE) analysis to provide a safety assessment of the structure.

Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs

Structure from motion (SfM) photogrammetry is an increasingly common technique for measuring landscape change over time by deriving 3D point clouds and surface models from overlapping photographs. Traditional change detection approaches require photos that are geotagged with a differential GPS (DGPS) location, which requires expensive equipment that can limit the ability of communities and researchers to perform frequent (i.e. daily, weekly, and/or monthly) surveys. Crowd-sourced photos can lower the barrier to entry and substantially increase the frequency of surveys, although such photos often lack accurate location information and can vary in quality. This paper presents a SfM approach for monitoring environmental change in high relief coastal environments that does not require all photos have DGPS location information and does not require field survey data. A 1.5 km section of coastal bluffs near the Elwha River Delta (Washington state) is used to demonstrate the efficacy of this approach. Photos of the bluff were collected with a digital SLR camera or phone camera while either on foot along the beach or from a boat as part of monitoring following removal of two large dams along the Elwha River during 2011–2013. Only 33% of photos had DGPS location information, whereas most photos had no location information or locations that were accurate to a couple of meters. All photos were processed using 3D, 4D, and fixed-floating (FF) SfM alignment methods and the resulting dense point clouds are used to compare the different alignment approaches with crowd-sourced photo sets. Results demonstrate that 4D and FF approaches are more likely to reconstruct and are more accurate than the 3D approach. While the 4D and FF have comparable accuracies, the FF approach is several orders of magnitude more efficient, as this method can leverage camera location information from relatively few photos to improve the accuracy of all aligned and derived products. Effectively utilizing crowd-sourced photos in SfM change detection can improve the frequency of surveying a landscape in a more cost-effective approach that also has potential for citizen-science engagement and communication. This is especially important for data-poor environments such as high-relief coastal cliffs and bluffs, where near-nadir imagery and LIDAR may fail to accurately capture near-vertical cliffs or bluff faces. Based on the analysis of different photo alignment and filtering approaches, we present suggested best practices for engaging citizen scientists in coastal cliff and bluff monitoring efforts through collecting photos amenable for SfM reconstruction.

Optical flow-based structure-from-motion for the reconstruction of epithelial surfaces

This paper details a novel optical flow-based structure from motion (SfM) approach for the reconstruction of surfaces with few textures using video sequences acquired under strong illumination changes. An original image search and grouping strategy allows to reconstruct each 3D scene point using a large set of 2D homologous points extracted from a reference image and its superimposed images acquired from different viewpoints. A variational optical flow scheme with a descriptor-based data term leads to a robust, accurate and dense homologous point determination between the image pairs. Thus, contrary to classical SfM usable for textured scenes, the proposed dense point cloud reconstruction algorithm requires neither a feature point tracking method nor any multi-view stereo technique. The performance of the proposed SfM approach is assessed on phantoms with known ground truth and on very complex patient data of various medical examinations and image modalities.

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m3 and 9 m3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg∙m−3) and laboratory measurements (1756 kg∙m−3) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

FORMAP: A SIMPLE AND FAST PHOTOGRAMMETRY FRAMEWORK FOR DIRECT GEO-REFERENCING SYSTEMS

Abstract. This paper presents FORMap (Fast Ortho Mapping) a simple, automatic, fast and accurate commercial photogrammetry processing software for Unmanned Aerial Vehicles (UAV) imagery equiped with Direct Georeferencing (DG) technology. DG technique allows user to directly geo-reference the acquisition without the use of Ground Control Points (GCP) by providing image external orientation (EO) parameters in a mapping frame. However, it requires a sensor of relatively high quality to provide an accurate EO with each image shot, which is somehow limited by the light weight of UAV payloads. FORMap makes use of EO information delivered by DG as an a priori information to accelerate its photogrammetric processing. We present the functionalities and some application of FORMap in the field of UAV mapping. We evaluate its accuracy and its robustness on several datasets. Test result shows that FORMap is robust for 3D scene reconstruction despite of inaccuracies of DG input data. It is also faster than standard digital photogrammetry solution based on SfM (Structure from Motion) approach and can provide orthophotos and dense point cloud in quasi real-time.

Underwater Structure from Motion for Cameras Under Refractive Surfaces

Structure from Motion (SfM), as a three-dimensional (3D) reconstruction technique, can estimate the structure of an object by using a single moving camera. Cameras deployed in underwater environments are generally confined to waterproof housings. Thus, the light rays entering the camera are refracted twice; once at the interface between the water and the camera housing, and again at the interface between the camera housing and air. Images captured from scenes in underwater environments are prone to, and deteriorate, from distortion caused by this refraction. Severe distortions in geometric reconstruction would be caused if the refractive distortion is not properly addressed. Here, we propose a SfM approach to deal with the refraction in a camera system including a refractive surface. The impact of light refraction is precisely modeled in the refractive model. Based on the model, a new calibration and camera pose estimation method is proposed. This proposed method assists in accurate 3D reconstruction using the refractive camera system. Experiments, including simulations and real images, show that the proposed method can achieve accurate reconstruction, and effectively reduce the refractive distortion compared to conventional SfM.

3D DIGITAL AND PHYSICAL RECONSTRUCTION OF A COLLAPSED DOME USING SFM TECHNIQUES FROM HISTORICAL IMAGES

Abstract. The historical images preserved in archives and in private collections represent not only a valuable documentation of objects belonging to Cultural Heritage; sometimes they are the only remained evidence of destroyed assets of our past. In the last few years, the improvement of the technologies in the framework of photogrammetric vision and the implementations of new Structure-from-Motion (SfM) algorithms allow to extract metric information’s from this kind of images in order to carry out a digital reconstruction of these lost masterpieces. The study presented in this paper aims to evaluate a SfM approach to perform the 3D reconstruction of a dome collapsed in 1971 by using historical images. The final goal is to provide not only a digital replica but also a physical reconstruction of a portion of the collapsed dome as a support for the recovered fragments of the fresco originally present on the surface of the dome.

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59–0.81) as the ruler height measurements (R2 = 0.58–0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.

Improved SfM-Based Indoor Localization with Occlusion Removal

This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.

UAVS TO ASSESS THE EVOLUTION OF EMBRYO DUNES

Abstract. The balance of a coastal environment is particularly complex: the continuous formation of dunes, their destruction as a result of violent storms, the growth of vegetation and the consequent growth of the dunes themselves are phenomena that significantly affect this balance. This work presents an approach to the long-term monitoring of a complex dune system by means of Unmanned Aerial Vehicles (UAVs). Four different surveys were carried out between November 2015 and November 2016. Aerial photogrammetric data were acquired during flights by a DJI Phantom 2 and a DJI Phantom 3 with cameras in a nadiral arrangement. GNSS receivers in Network Real Time Kinematic (NRTK) mode were used to frame models in the European Terrestrial Reference System. Processing of the captured images consisted in reconstruction of a three-dimensional model using the principles of Structure from Motion (SfM). Particular care was necessary due to the vegetation: filtering of the dense cloud, mainly based on slope detection, was performed to minimize this issue. Final products of the SfM approach were represented by Digital Elevation Models (DEMs) of the sandy coastal environment. Each model was validated by comparison through specially surveyed points. Other analyses were also performed, such as cross sections and computing elevation variations over time. The use of digital photogrammetry by UAVs is particularly reliable: fast acquisition of the images, reconstruction of high-density point clouds, high resolution of final elevation models, as well as flexibility, low cost and accuracy comparable with other available techniques.

Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods

AbstractIn this study we evaluate the extent to which accurate topographic data can be obtained by applying Structure from Motion (SfM) photogrammetric methods to archival imagery. While SfM has proven valuable in photogrammetric applications using specially acquired imagery (e.g. from unmanned aerial vehicles), it also has the potential to improve the precision of topographic data and the ease with which can be produced from historical imagery. We evaluate the application of SfM to a relatively extreme case, one of low relative relief: a braided river–floodplain system. We compared the bundle adjustments of SfM and classical photogrammetric methods, applied to eight dates. The SfM approach resulted in data quality similar to the classical approach, although the lens parameter values (e.g. focal length) recovered in the SfM process were not necessarily the same as their calibrated equivalents. Analysis showed that image texture and image overlap/configuration were critical drivers in the tie‐point generation which impacted bundle adjustment quality. Working with archival imagery also illustrated the general need for the thorough understanding and careful application of (commercial) SfM software packages. As with classical methods, the propagation of (random) error in the estimation of lens and exterior orientation parameters using SfM methods may lead to inherent systematic error in the derived point clouds. We have shown that linear errors may be accounted for by point cloud registration based on a reference dataset, which is vital for the further application in quantitative morphological analyses when using archival imagery. Copyright © 2016 John Wiley & Sons, Ltd.

Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR

We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C∙ha−1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR—albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires.

An SfM Algorithm With Good Convergence That Addresses Outliers for Realizing Mono-SLAM

Monocular simultaneous localization and mapping (mono-SLAM) is a key component of autonomous robot visual navigation. Recently, the structure from motion (SfM) approach has become an attractive means of implementing mono-SLAM because of its high accuracy; although, in this application, the SfM approach must be operable in real time and robust to outliers. However, because of strong nonlinearity, conventional SfM methods, such as bundle adjustment, must consider multiple initial values to obtain the globally optimal result, which is time consuming. In this paper, a novel iterative SfM algorithm based on the object-space objective function is proposed. To improve the method’s robustness to outliers and to incorporate the information obtained from other types of sensors, an approach to closely integrate the proposed SfM algorithm with extended Kalman filter-based SLAM is proposed. Experimental results using both synthesized and real data are consistent with our theory, and verify that the main advantage of the proposed SfM algorithm is its good convergence. The algorithm is therefore particularly appropriate for realizing mono-SLAM, because when rotations can be obtained approximately using gyroscope information, the algorithm is globally convergent from any initial value.

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired under leaf-on canopy conditions. A 3D point cloud was generated from aerial photographs using the SfM approach and converted to a digital surface model (DSMP). We also created a DSM from airborne LiDAR data (DSML). From each of DSMP and DSML, we constructed digital terrain models (DTM), which are DTMP and DTML, respectively. We created four CHMs, which were calculated from (1) DSMP and DTMP (CHMPP); (2) DSMP and DTML (CHMPL); (3) DSML and DTMP (CHMLP); and (4) DSML and DTML (CHMLL). Then, we estimated AGB using these CHMs. The model using CHMLL yielded the highest accuracy in four CHMs (R2 = 0.94) and was comparable to the model using CHMPL (R2 = 0.93). The model using CHMPP yielded the lowest accuracy (R2 = 0.79). In conclusion, AGB can be estimated from CHM derived from aerial photographs using the SfM approach in the tropics. However, to accurately estimate AGB, we need a more accurate DTM than the DTM derived from aerial photographs using the SfM approach.

Reconstructing flash flood magnitudes using ‘Structure-from-Motion’: A rapid assessment tool

• Structure-from-Motion permits rapid field survey as part of post-flood analysis. • High water marks can be extracted from the resulting model directly. • SfM coupled with 2D hydraulic modelling offers estimates of peak flood magnitude. Accurate records of flash flood magnitudes are required to inform flood forecasting and planning. However, whilst a distributed flood survey is desirable to capture spatial heterogeneity in peak water surface elevation, the field time required for a distributed survey often limits the spatial coverage of such reconstructions. For the first time, we demonstrate the application of Structure-from-Motion (SfM) with Multi-View Stereo (MVS) to reconstruct the magnitude of a flash flood. This approach required only standard digital photographs and ground control points, took only ∼30 min in the field, and can be embedded within existing protocols easily. We validated the method against a conventional dGPS survey in three stages: (i) comparison of topographic data revealed that SfM was accurate to within 0.1 m; (ii) high water marks extracted from the SfM model were within 0.25 m of those surveyed in the field with no consistent over or under-estimate; (iii) peak discharge reconstructed from a two-dimensional hydraulic model was within the range of more conventional estimates. With low uncertainty in our terrain model and our reconstructed flood water surface, we highlight the added value of the SfM approach for incorporating reach scale spatial variability into hydraulic reconstructions.

Evaluation of structure from motion for soil microtopography measurement

AbstractRecent developments in low‐cost structure‐from‐motion (SfM) technologies offer new opportunities for geoscientists to acquire high‐resolution soil microtopography data at a fraction of the cost of conventional techniques. However, these new methodologies often lack easily accessible error metrics and hence are difficult to evaluate. In this research, a framework was developed to evaluate a SfM approach for soil microtopography measurement through assessment of uncertainty sources and quantification of their potential impact on overall 3D reconstruction. Standard deviations of camera interior orientation parameters estimated from SfM self‐calibration within five different soil surface models were several orders of magnitude larger than precisions expected from pattern‐based camera calibration. Sensitivity analysis identified the principal point position as the dominant source of calibration‐induced uncertainty. Overall, surface elevation values estimated from both technologies were similar in magnitude with a root mean square (RMS) of elevation difference of 0·2 mm. Nevertheless, the presence of deformation in either SfM or traditional photogrammetric point clouds highlights the importance of quality assurance safeguards (such as a judicious choice of control points) in SfM workflows for soil microtopography applications.

An efficient approach for 2D to 3D video conversion based on structure from motion

With the popularity of 3D films, the conversion of existing 2D videos to 3D videos has attracted a wide interest in 3D content production. In this paper, we present an efficient approach for 2D to 3D video conversion based on structure from motion (SFM). The key contributions include a piece-wise SFM approach and a novel nonlinear depth warping considering the characteristics of stereoscopic 3D. The dense depth maps are generated and further refined with color segmentation. Experiments show that the proposed approach can yield more visually satisfactory results.