Multi-View Stereo: A Tutorial

Furukawa, Yasutaka; Hernández, C.

doi:10.1561/0600000052

Cited by 411 publications

(210 citation statements)

References 32 publications

(39 reference statements)

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“…The principle of pixel-based 3D reconstruction is that the software finds the corresponding image points between overlapping images, then calculates the position and orientation of capturing and determines the 3D coordinates of the object's points based on spatial triangulation (Moons et al, 2008, Furukawa andHernandez, 2015). The technique enables 1 From technical specifications provided by manufacturers 2 Prices from suppliers' websites matching color information to the points.…”

Section: Figure 1 the Test Objectmentioning

confidence: 99%

Capturing Fine Details Involving Low-Cost Sensors –A Comparative Study

Rehány

Barsi

Lovas

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Capturing the fine details on the surface of small objects is a real challenge to many conventional surveying methods. Our paper discusses the investigation of several data acquisition technologies, such as arm scanner, structured light scanner, terrestrial laser scanner, object linescanner, DSLR camera, and mobile phone camera. A palm-sized embossed sculpture reproduction was used as a test object; it has been surveyed by all the instruments. The result point clouds and meshes were then analyzed, using the arm scanner's dataset as reference. In addition to general statistics, the results have been evaluated based both on 3D deviation maps and 2D deviation graphs; the latter allows even more accurate analysis of the characteristics of the different data acquisition approaches. Additionally, own-developed local minimum maps were created that nicely visualize the potential level of detail provided by the applied technologies. Besides the usual geometric assessment, the paper discusses the different resource needs (cost, time, expertise) of the discussed techniques. Our results proved that even amateur sensors operated by amateur users can provide high quality datasets that enable engineering analysis. Based on the results, the paper contains an outlook to potential future investigations in this field.

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Section: Figure 1 the Test Objectmentioning

confidence: 99%

Capturing Fine Details Involving Low-Cost Sensors –A Comparative Study

Rehány

Barsi

Lovas

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The census transform is a non-parametric local transform, which was first proposed by Zabih and Woodfill [16]. It transforms an image into the binary image which is invariant to illumination and viewpoint changes as mentioned by Furukawa et al [17]. Given an image window, the census transform computes a binary string which denotes whether the pixel intensity of each neighboring pixel is greater than the center pixel, as illustrated in Fig.…”

Section: Initial Depth Estimation Using Census Transformmentioning

confidence: 99%

Depth Map Estimation Using Census Transform for Light Field Cameras

Tomioka

Mishiba

Oyamada

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYDepth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.

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“…However existing approaches to object detection struggle with these non-Lambertian objects [6,21,3] because the shiny reflections create false images and sharp gradients that change dramatically with camera position, fooling methods that are based on only a single camera image.…”

Section: Introductionmentioning

confidence: 99%

“…An important area of releated work is multi-view stereo, which fuses information from multiple calibrated camera images in order to create a 3D reconstruction of the scene [6]. Multi-view stereo typically uses correspondence of features across frames as the main cue for 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Time-Lapse Light Field Photography for Perceiving Non-Lambertian Scenes

Oberlin

Tellex

2017

Robotics: Science and Systems XIII

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Abstract-Robust robotic perception and manipulation of household objects requires the ability to detect, localize and manipulate a wide variety of objects, which may be mirror reflective like polished metal, glossy like smooth plastic, or transparent like glass; for example, picking a metal fork out of a sink full of running water or screwing a metal nut onto a bolt. Existing perceptual approaches based on photographs only take into account the average intensity of light arriving at each pixel from one direction, which limits their ability to account for these non-Lambertian scenes. To address this problem, we demonstrate time-lapse light field photography with an eye-inhand camera of a manipulator robot. An eye-in-hand robot can capture both the intensity of rays, as in a conventional photograph, as well as the direction of the rays. We present a formal model for robotic light-field photography that fits into a probabilistic robotics framework. Using this model, we can synthesize orthographic photographs, remove specular highlights from those photographs, and perform 3D reconstruction with a monocular camera by finding approximate maximum-likelihood estimates. This information can be used to detect, localize and manipulate non-Lambertian objects in non-Lambertian scenes: our approach enables the Baxter robot to pick a shiny metal fork out of a sink filled with running water 24/25 times, as well as to localize objects well enough to screw a nut onto a quarter inch bolt. The techniques in this paper point the way toward new approaches to robotic perception that leverage a robot's ability to move its camera to infer the state of the external world.

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Multi-View Stereo: A Tutorial

Cited by 411 publications

References 32 publications

Capturing Fine Details Involving Low-Cost Sensors –A Comparative Study

Capturing Fine Details Involving Low-Cost Sensors –A Comparative Study

Depth Map Estimation Using Census Transform for Light Field Cameras

Time-Lapse Light Field Photography for Perceiving Non-Lambertian Scenes

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