Time-of-Flight Cameras and Microsoft Kinect™

Mutto, Carlo Dal; Zanuttigh, Pietro; Cortelazzo, G.M.

doi:10.1007/978-1-4614-3807-6

Cited by 122 publications

(82 citation statements)

References 1 publication

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“…Arguably the most popular approach for pattern projection is utilizing spatial-multiplexing coding technology with a static pattern for fast, accurate, and low-cost depth processing. Spatial-multiplexing structured-light depth sensors are also generally robust for projector and detector noise and non-idealities, color and gray-level distortion, non-ideal surfaces, external illumination, and distortion from motion [77]. Hence, this class of depth sensors is the primary focus in this dissertation, and is explained in more detail in the next two sections and Chapter 3.…”

Section: Depth Sensor Classesmentioning

confidence: 99%

“…Nguyen et al imprecisely relate their models to the sensor's point spread function (PSF), which was characterized as the distribution of absolute errors from the distance of the observed edges to fitted lines. Instead, Mutto et al [77] correctly explain that IR dots projected on excessively slanted surfaces are spread over more than two pre-downsampled pixels, where shifted dots characterized by various disparities within the correlation window can lead to a poor match from the correspondence algorithm. It should be noted that due to the nature of empirical models, the estimates of model coefficients are strongly dependent on the measured data sets, and errors from any hidden variables not accounted for will be mixed into the estimated parameter values.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, all reported error models are directly formed from depth images, and overlook the intermediate IR images that are used to generate them. As many researchers point out ( [77], [71], etc. ), various sources of error (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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Accurate 6D object pose estimation, as well as other model-based shape matching objectives such as object detection, classification, and shape inspection, is prominent and necessary in a large number of domains and applications. This includes household and robotics applications to automate various tasks, where input depth measurements are aligned to a corresponding model of the object, such as a CAD that is composed of several interconnected parts, depending on the model's level of detail and complexity. Industrial metrology systems also compare the shapes of engineered components and equipment to their corresponding 3D models for quality assessment and anomaly detection; this often requires very costly and high resolution projection and detection mechanisms. Structuredlight laser scanners have, however, emerged as a viable option to provide cheap and reasonably accurate depth estimates, which have recently become commodity-priced within this decade due to the maturity of the technology. These sensors are also robust for semi-transparent and reflective surfaces, dynamic scenery, ambient background light, and temperature drift. Certain structured-light light coding methods unfortunately need to implement a complicated, non-invertible transformation of a distorted light pattern to generate the pixel-by-pixel depth estimates, thereby leading to a loss of information. This can result in poorly estimated or even missing depth data, especially when the object has small 3D features or non-ideal surface properties. Moreover, the informative rigid body constraint is blurred when the nonlinear 2D depth image to 3D pseudo-measurement point cloud transformation is applied, which is the domain where most model-based shape matching methods operate.Alternatively, estimating pose from information early in the structured-light sensing/processing chain has the potential to alleviate errors induced in subsequent nonlinear processing steps that are in fact locally scene dependent. This motivates one of the main contributions from this dissertation, which presents an asymptotically optimal maximum likelihood estimation method that operates directly on the raw output IR images. This is made possible by an extensive study on a commonly utilized commodity-priced structured-light sensor, which contributed to the proposed high-fidelity IR and depth image predictor and simulator that models the physics of the transmit and receive optics,

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Section: Depth Sensor Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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“…Early systems were primarily based stereovision (comparing images from two cameras separated by a known baseline) [7], but have recently given way to active sensors. Of particular note are time-of-flight cameras [8,9], LIDARs [6], and structured light sensors (as exemplified by the Microsoft Kinect) [4,9].…”

Section: Introductionmentioning

confidence: 99%

Glidar: An OpenGL-based, Real-Time, and Open Source 3D Sensor Simulator for Testing Computer Vision Algorithms

Woods

Christian

2016

J. Imaging

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3D sensors such as LIDARs, stereo cameras, time-of-flight cameras, and the Microsoft Kinect are increasingly found in a wide range of applications, including gaming, personal robotics, and space exploration. In some cases, pattern recognition algorithms for processing depth images can be tested using actual sensors observing real-world objects. In many situations, however, it is common to test new algorithms using computer-generated synthetic images, as such simulations tend to be faster, more flexible, and less expensive than hardware tests. Computer generation of images is especially useful for Monte Carlo-type analyses or for situations where obtaining real sensor data for preliminary testing is difficult (e.g., space applications). We present GLIDAR, an OpenGL and GL Shading Language-based sensor simulator, capable of imaging nearly any static three-dimensional model. GLIDAR allows basic object manipulations, or may be connected to a physics simulator for more advanced behaviors. It permits publishing to a TCP socket at high frame-rates or can save to PCD (point cloud data) files. The software is written in C++, and is released under the open source BSD license.

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“…Its inner structure is the almost same as original cameras, and it is equipped with some infrared LEDs and the special image sensor. It measures distance by a time-of-flight method (Mutto et al, 2012). Since each element of the image sensor measures distance independently, the range camera can acquire a dense-range image in a single shot.…”

Section: Introductionmentioning

confidence: 99%

Development of Integration and Adjustment Method for Sequential Range Images

Nagara

Fuse

2015

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

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Commission V, WG V/4KEY WORDS: 3D data, Range image, Point cloud, ICP algorithm, Self-calibration bundle adjustment ABSTRACT:With increasing widespread use of three-dimensional data, the demand for simplified data acquisition is also increasing. The range camera, which is a simplified sensor, can acquire a dense-range image in a single shot; however, its measuring coverage is narrow and its measuring accuracy is limited. The former drawback had be overcome by registering sequential range images. This method, however, assumes that the point cloud is error-free. In this paper, we develop an integration method for sequential range images with error adjustment of the point cloud. The proposed method consists of ICP (Iterative Closest Point) algorithm and self-calibration bundle adjustment. The ICP algorithm is considered an initial specification for the bundle adjustment. By applying the bundle adjustment, coordinates of the point cloud are modified and the camera poses are updated. Through experimentation on real data, the efficiency of the proposed method has been confirmed.

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Time-of-Flight Cameras and Microsoft Kinect™

Cited by 122 publications

References 1 publication

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Glidar: An OpenGL-based, Real-Time, and Open Source 3D Sensor Simulator for Testing Computer Vision Algorithms

Development of Integration and Adjustment Method for Sequential Range Images

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