Operating Principles of Structured Light Depth Cameras

Zanuttigh, Pietro; Marin, Giulio; Mutto, Carlo Dal; Dominio, Fabio; Minto, Ludovico; Cortelazzo, G.M.

doi:10.1007/978-3-319-30973-6_2

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…The overall performance of a depth camera system is highly influenced by the used pattern projector illuminator. The Intel® RealSense cameras partially overcomes the limitation of temporal multiplexing [23], [25] illuminators (i.e. unreliable in case of fast moving scenes), by leveraging the innovative MEMS mirror technology [25] to speed up the pattern frames projection time.…”

Section: Intel® Sr300 Depth Cameramentioning

confidence: 99%

“…2) Systematic Depth Offset Assessment: As reported in [23] the configuration of structured light system is "flexible" this meaning that either a single camera (as in the case of PrimeSense products and the Intel® F200 and SR300) or two cameras (as in the case of the Intel® R200) can be used to acquire the scene. However, all devices can be considered as different members of the same family in the unified framework proposed in [28] by which a system with a single camera and an illuminator is equivalent to a system with two rectified cameras and an illuminator.…”

Section: Systematic Errorsmentioning

confidence: 99%

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On the Performance of the Intel SR300 Depth Camera: Metrological and Critical Characterization

et al. 2017

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Specifically conceived for applications related to face analytics and tracking, scene segmentation, hand/finger tracking, gaming, augmented reality, and RGB-D cameras are nowadays used even as 3-D scanners. Despite depth cameras' accuracy and precision are not comparable with professional 3-D scanners, they still constitute a promising device for reverse engineering (RE) applications in the close range, due to their low cost. This is particularly true for more recent devices, such as, for instance, the RealSense SR300, which promises to be among the best performing close range depth cameras in the market. Given the potentiality of this new device, and since to date a deep investigation on its performances has not been assessed in scientific literature, the main aim of this paper is to characterize and to provide metrological considerations on the Intel RealSense SR300 depth sensor when this is used as a 3-D scanner. To this end, the device sensor performances are first assessed by applying the existing normative guidelines (i.e. the one published by the Association of German Engineers-Verein Deutscher Ingenieure-VDI/VDE 2634) both to a set of raw captured depth data and to a set acquired with optimized setting of the camera. Then, further assessment of the device performances is carried out by applying some strategies proposed in the literature using optimized sensor setting, to reproduce "real life" conditions for the use as a 3-D scanner. Finally, the performance of the device is critically compared against the performance of latest short-range sensors, thus providing a useful guide, for researchers and practitioners, in an informed choice of the optimal device for their own RE application.

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Section: Intel® Sr300 Depth Cameramentioning

confidence: 99%

Section: Systematic Errorsmentioning

confidence: 99%

On the Performance of the Intel SR300 Depth Camera: Metrological and Critical Characterization

et al. 2017

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“…Mallick et al [35] obtained similar results. The PrimeSense sensors are based on the same structured light approach as the Kinect v1 (which uses PrimeSense's technology [62]). The noise characteristics of both devices are thus similar.…”

Section: Depth Sensor Noise Characteristicsmentioning

confidence: 99%

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

Golodetz

Cavallari

Lord

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Globally consistent reconstructions produced by our approach, based on the Flat, House and Lab subsets of our dataset.Abstract-Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases. These are good reasons to want instead to capture several smaller sub-scenes that can be joined to make the whole scene. Achieving this has traditionally been difficult: joining sub-scenes that may never have been viewed from the same angle requires a high-quality camera relocaliser that can cope with novel poses, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances, however, have significantly improved our ability to capture medium-sized sub-scenes with little to no tracking drift: real-time globally consistent reconstruction systems can close loops and re-integrate the scene surface on the fly, whilst new visual-inertial odometry approaches can significantly reduce tracking drift during live reconstruction. Moreover, high-quality regression forest-based relocalisers have recently been made more practical by the introduction of a method to allow them to be trained and used online. In this paper, we leverage these advances to present what to our knowledge is the first system to allow multiple users to collaborate interactively to reconstruct dense, voxel-based models of whole buildings using only consumer-grade hardware, a task that has traditionally been both time-consuming and dependent on the availability of specialised hardware. Using our system, an entire house or lab can be reconstructed in under half an hour and at a far lower cost than was previously possible.Moreover, the risk of transient changes to the scene (e.g. people moving around) goes up as the capture time increases, corrupting the model and forcing the user to restart the capture. There are thus good reasons to want to split the capture into several shorter sequences, which can be captured either over multiple sessions or in parallel (by multiple users) and then joined to make the whole scene.Achieving this has traditionally been difficult: joining the sub-scenes requires the ability to accurately determine the relative transformations between them (a problem that can be expressed as camera relocalisation), even though the areas in which they overlap may never have been viewed from the same angles, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances, however, have significantly improved our ability to capture consistent, medium-sized sub-scenes, e.g. by closing loops and re-integrating the scene surface on the fly [17], which yields accurate poses for individual frames once loops have been closed, or by combining visual and inertial cues using an extended Kalman filter [28] to achieve accurate camera tracking during live reconstruction. Moreo...

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“…A depth map is obtained by decoding pattern signals from the captured image for triangulation. Structured light 3D camera systems are widely used in the field of robotics [ 14 , 15 ], inspection of printed circuit board (PCB) [ 16 ], industrial automation [ 16 , 17 , 18 ], saving cultural heritage [ 19 ], examination of arc welding pools [ 20 ], object recognition [ 21 ], and dental surgery [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

Atif¹,

Lee²

2018

Sensors

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The quality of the captured point cloud and the scanning speed of a structured light 3D camera system depend upon their capability of handling the object surface of a large reflectance variation in the trade-off of the required number of patterns to be projected. In this paper, we propose and implement a flexible embedded framework that is capable of triggering the camera single or multiple times for capturing single or multiple projections within a single camera exposure setting. This allows the 3D camera system to synchronize the camera and projector even for miss-matched frame rates such that the system is capable of projecting different types of patterns for different scan speed applications. This makes the system capturing a high quality of 3D point cloud even for the surface of a large reflectance variation while achieving a high scan speed. The proposed framework is implemented on the Field Programmable Gate Array (FPGA), where the camera trigger is adaptively generated in such a way that the position and the number of triggers are automatically determined according to camera exposure settings. In other words, the projection frequency is adaptive to different scanning applications without altering the architecture. In addition, the proposed framework is unique as it does not require any external memory for storage because pattern pixels are generated in real-time, which minimizes the complexity and size of the application-specific integrated circuit (ASIC) design and implementation.

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Operating Principles of Structured Light Depth Cameras

Cited by 12 publications

References 7 publications

On the Performance of the Intel SR300 Depth Camera: Metrological and Critical Characterization

On the Performance of the Intel SR300 Depth Camera: Metrological and Critical Characterization

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

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