Evaluation of close-range stereo matching algorithms using stereoscopic measurements

The performance of binocular stereo reconstruction is highly dependent on the quality of the stereo matching result. In order to evaluate the performance of different stereo matchers, several quality metrics have been developed based on quantifying error statistics with respect to a set of independent measurements usually referred to as ground truth data. However, such data are frequently not available, particularly in practical applications or planetary data processing. To address this, we propose a ground truth independent evaluation protocol based on manual measurements. A stereo visualization tool has been specifically developed to evaluate the quality of the computed correspondences. We compare the quality of disparity maps calculated from three stereo matching algorithms, developed based on a variation of GOTCHA, which has been used in planetary robotic rover image reconstruction at UCL-MSSL (Otto and Chau, 1989). From our evaluation tests with the images pairs from Mars Exploration Rover (MER) Pancam and the field data collected in PRoViScout 2012, it has been found that all three processing pipelines used in our test (NASA-JPL, JR, UCL-MSSL) trade off matching accuracy and completeness differently. NASA-JPL's stereo pipeline produces the most accurate but less complete disparity map, while JR's pipeline performs best in terms of the reconstruction completeness

Monitoring of beaches and sand dunes using digital aerial photography with direct georeferencing

This paper presents an efficient methodology for coastal monitoring based on digital aerial photography acquired periodically, using a Zeiss-Intergraph DMC camera, with a spatial resolution of 10 cm. Images are delivered with exterior orientation given by the direct georeferencing system used by the company providing the images. An assessment of those exterior orientation parameters was done using field check points. It could be concluded that the positional accuracy of 3D coordinates determined by photogrammetric means using the exterior orientation elements has root mean square errors better than 30 cm, which can be accepted for the purposes of this type of work. This level of accuracy was achieved for four different flights. It was decided not to do an aerial triangulation. DSMs were obtained by stereo-matching (least-squares with region growing) using program DPCOR of the software package BLUH. Sand areas are normally very bright and difficult for the matching. Since images were acquired with relatively small sun elevation the automatic extraction of a DSM was very successful. Using check points the vertical accuracy of the DSMs was found to be 30 cm or better. Orthoimages were created using the extracted DSM. The resulting datasets for the four different dates were integrated in a GIS and the method proved to be a very useful tool for the detection of areas where significant volumetric changes occurs and for the quantification of those beach changes

Correspondência eficiente de descritores SIFT para construção de mapas densos de pontos homólogos em imagens de sensoriamento remoto [Efficient matching steps of the SIFT for constructing a dense map of conjugate points in remote sensing images]

Area-based automatic image matching combined with a region-growing technique are able to provide a dense and accurate set of corresponding points. However, the region-growing process may stop at image patches where the horizontal x-parallax has an abrupt change. In such cases new pairs of corresponding points (seeds) must be provided, usually by a human operator. The region growing procedure restarts then from the new seed points. Depending upon the type of image and the 3D-structure of the mapped area, the human intervention may be considerable. A fully automatic alternative for finding conjugate points in stereo pairs was proposed by the authors in a prior work. The method combines the scale invariant feature transform, the Least-Squares matching and the region-growing technique. This work presents an extension of that technique. Basically, improvements in the matching step of the SIFT algorithm are proposed, which explores properties of stereo images produced by aerial and orbital sensors. Experiments conducted on stereo pairs from both airborne and satellite imagery show that the benefit of the proposed changes is twofold. Firstly, the number of true substantially with no significant increase in the proportion Secondly, the computational load is dramatically reduced.Métodos automáticos de localização de pontos homólogos em imagens digitais baseados em área, combinados com técnicas de crescimento de região, são capazes de produzir uma malha densa e exata de pontos homólogos. Entretanto, o processo de crescimento de região pode ser interrompido em regiões da imagem, cuja paralaxe no eixo horizontal apresenta variação abrupta. Essa situação geralmente é causada por uma descontinuidade na superfície ou espaço-objeto imageado, tal como um prédio numa cena urbana ou um paredão de exploração de uma mina a céu aberto. Nesses casos, novos pares de pontos homólogos (sementes) devem ser introduzidos, normalmente por um operador humano, a partir dos quais o processo é reiniciado. Dependendo do tipo da imagem utilizada e da estrutura 3D da região mapeada, a intervenção humana pode ser considerável. Uma alternativa totalmente automatizada em que se combinam as técnicas SIFT (Scale Invariant Feature Transform), pareamento por mínimos quadrados e crescimento de região foi proposta anteriormente pelos autores. O presente trabalho apresenta uma extensão a essa técnica. Basicamente, propõem-se alterações na etapa de correspondência do SIFT, que exploram características de estereogramas produzidos por sensores aéreos e orbitais. Avaliações experimentais demonstram que as modificações propostas trazem dois tipos de benefícios. Em primeiro lugar, obtém-se um aumento do número de pontos homólogos encontrados, sem aumento correspondente na proporção de falsas correspondências. Em segundo lugar, a carga computacional é reduzida substancialmente

Automated localisation of Mars rovers using co-registered HiRISE-CTX-HRSC orthorectified images and wide baseline Navcam orthorectified mosaics

We present a wide range of research results in the area of orbit-to-orbit and orbit-to-ground data fusion, achieved within the EU-FP7 PRoVisG project and EU-FP7 PRoViDE project. We focus on examples from three Mars rover missions, i.e. MER-A/B and MSL, to provide examples of a new fully automated offline method for rover localisation. We start by introducing the mis-registration discovered between the current HRSC and HiRISE datasets. Then we introduce the HRSC to CTX and CTX to HiRISE co-registration workflow. Finally, we demonstrate results of wide baseline stereo reconstruction with fixed mast position rover stereo imagery and its application to ground-to-orbit co-registration with HiRISE orthorectified image. We show examples of the quantitative assessment of recomputed rover traverses, and extensional exploitation of the co-registered datasets in visualisation and within an interactive web-GIS

Complementary geometric and optical information for match-propagation-based 3D reconstruction

International audienceIn this work, we consider the problem of propagation-based matching for 3D reconstruction, which deals with expanding a limited set of correspondences towards a quasi-dense map across two views. In general, propagation based methods capture well the scene structure. However, the recovered geometry often presents an overall choppy na-ture which can be attributed to matching errors and abrupt variations in the estimated local affine transformations. We propose to control the reconstructed geometry by means of a local patch fitting which corrects both the matching locations and affine transformations throughout the propagation process. In this way, matchings that propagate from geo-metrically consolidated locations bring coherence to both positions and affine transformations. Results of our approach are not only more visu-ally appealing but also more accurate and complete as substantiated by results on standard benchmarks

Joint View Triangulation for Two Views

International audienceWe propose the Joint View Triangulation, which coherently models all visible and partially occluded patches within n views of a scene (rigid or not). It is built from an underlying dense matching and can be used for any application requiring discrete and ef cient representation of deformation and displacement between views. First robustness has to deal the unavoidable matching errors. Secondly matched and half occluded areas should be separated in each view to allow different processes on them. Finally, the elements of the structure which represent the matched area of each view pair should be in correspondence. This ensures a global coherence of the data and avoid redundant processes. In fact, we merely expect to an approximate but coherent structure, because of the nite precision of the images and bad matches. This paper deals only with the two view case but also applies the joint view triangulation to morphing between real image pairs with large camera displacement

Enhancement Of Stereo Imagery By Artificial Texture Projection Generated Using A Lidar

Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image