Performance Analysis of a Low-Cost Triangulation-Based 3d Camera: Microsoft Kinect System

Chow, Jacky C. K.; Ang, Kathleen; Lichti, Derek D.; Teskey, W. F.

doi:10.5194/isprsarchives-xxxix-b5-175-2012

Cited by 34 publications

(30 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, Stoyanov et al (2011) evaluate the accuracy of a Kinect in comparison with a laser scanner using the Three-Dimensional Normal Distributions Transform (3DNDT), which is a spatial representation accuracy evaluation technique, and conclude that the Kinect sensor performs well within 3.5-m distances. In Chow et al (2012), a 3D reconstruction model of a mannequin is computed and compared with a laser scanner, and an RMSE of 11 mm is observed. In sum, previous studies reviewed herein conclude that the depth measurement and resolution of the Kinect are promising within a short range (e.g., 3 m), though not as accurate as those of a laser scanner, particularly in longer ranges.…”

Section: Previous Work On the Performance Evaluation Of An Rgb-d Sensormentioning

confidence: 99%

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

et al. 2013

View full text Add to dashboard Cite

Background: For construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in daily management practice. Methods: In an effort to address the issue, this paper investigates the performances of a cost-effective and portable RGB-D sensor, based on recent research efforts extended from our previous study. The performance of an RGB-D sensor is evaluated in terms of (1) the 3D positions of the body parts tracked by the sensor, (2) the 3D rotation angles at joints, and (3) the impact of the RGB-D sensor's accuracy on motion analysis. For the assessment, experimental studies were undertaken to collect motion capture datasets using an RGB-D sensor and a markerbased motion capture system, VICON, and to analyze errors as compared with the VICON used as the ground truth. As a test case, 25 trials of ascending and descending during ladder climbing were recorded simultaneously with both systems, and the resulting motion capture datasets (i.e., 3D skeleton models) were temporally and spatially synchronized for their comparison. Results: Through the comparative assessment, we found a discrepancy of 10.7 cm in the tracked locations of body parts, and a difference of 16.2 degrees in rotation angles. However, motion detection results show that the inaccuracy of an RGB-D sensor does not have a considerable effect on action recognition in the experiment. Conclusions: This paper thus provides insight into the accuracy of an RGB-D sensor on motion capture in various measures and directions of further research for the improvement of accuracy.

show abstract

Section: Previous Work On the Performance Evaluation Of An Rgb-d Sensormentioning

confidence: 99%

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The remaining variance in data can be attributed to random speckle n ∼ I and detector noise n ∼ , which are assumed zero mean and independent for the fit. As Chow et al [24] point out, indoor lighting conditions and object surface color have little effect on the detected IR intensity. Therefore, the constant ambient intensity can be attributed to background thermal radiation in the experimental setup.…”

Section: Formulating the Ir Intensity Modelmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

View full text Add to dashboard Cite

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,

show abstract

“…the Microsoft Kinect ® -see Chow et al, 2012). The advantage of using such device is their lowcost (about one hundred euros for gaming devices and up to one thousands for other systems), although they require the user to do sensor calibration on his own for performance improvement (Menna et al, 2011).…”

Section: Active 3d Imaging Sensorsmentioning

confidence: 99%

Photogrammetry in Experiments for Hydrogeological Risk Assessment

Barazzetti

Scaioni

Feng

et al. 2014

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

View full text Add to dashboard Cite

ABSTRACT:The construction of scaled-down simulation platforms is largely used to support investigations for the assessment of hydrological risk. Achieved outcomes can be integrated and assimilated to numerical analyses for the study of unstable slope collapse, debris transport, and hydrological modeling in general. During design of such simulation platforms, a relevant role has to be given to the spatial sensor network (SSN) to deploy, which is in charge of collecting geo-referenced, quantitative information during experiments. Photogrammetry (including 3D imaging sensors) can play an important role in SSN owing to its capability of collecting 2D images and 3D point clouds data covering wide surfaces without any contact. Different kinds of metric measurements can be then extracted from datasets. The aim of this paper is to give an overview and some examples on the potential of photogrammetry in hydrogeological experiments. After a general introduction on a few preliminary issues (sensors, calibration, ground reference, usage of imaging or ranging sensors), potential applications are classified into 2D and 3D categories. Examples are focused on a scaled-down landslide simulation platform developed at Tongji University (Shanghai, P.R. China).

show abstract

Performance Analysis of a Low-Cost Triangulation-Based 3d Camera: Microsoft Kinect System

Cited by 34 publications

References 20 publications

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Photogrammetry in Experiments for Hydrogeological Risk Assessment

Contact Info

Product

Resources

About