A novel RGB-Z camera for high-quality motion picture applications

Hach, Thomas; Steurer, Johannes

doi:10.1145/2534008.2534020

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…To evaluate our method on real data, we utilize data from a recently designed RGBD camera prototype (Hach & Steurer, 2013) to capture a frame-synchronized RGBD sequence of a static scene. Figure 1 schematically depicts this camera prototype on the left while the optical concept is shown on the right.…”

Section: Rgbd Camera Prototypementioning

confidence: 99%

“…Hence, we decided to use a dynamic RGBD ground truth sequences from the dataset provided by Richardt et al (Richardt, Orr, Davies, & Criminisi, 2010), including computer-generated matching RGB images and depth maps. For evaluation of denoising we add zero-mean white Gaussian noise in several experiments with increasing noise standard deviations, which have been chosen to compare to realistic noise levels (Hach & Steurer, 2013), (Hach, Seybold, & Böttcher, 2015b).…”

Section: Ground Truth Datamentioning

confidence: 99%

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Spatio-Temporal Denoising for Depth Map Sequences

Hach

Seybold

2016

International Journal of Multimedia Data Engineering and Management

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This paper proposes a novel strategy for depth video denoising in RGBD camera systems. Depth map sequences obtained by state-of-the-art Time-of-Flight sensors suffer from high temporal noise. Hence, all high-level RGB video renderings based on the accompanied depth maps' 3D geometry like augmented reality applications will have severe temporal flickering artifacts. The authors approached this limitation by decoupling depth map upscaling from the temporal denoising step. Thereby, denoising is processed on raw pixels including uncorrelated pixel-wise noise distributions. The authors' denoising methodology utilizes joint sparse 3D transform-domain collaborative filtering. Therein, they extract RGB texture information to yield a more stable and accurate highly sparse 3D depth block representation for the consecutive shrinkage operation. They show the effectiveness of our method on real RGBD camera data and on a publicly available synthetic data set. The evaluation reveals that the authors' method is superior to state-of-the-art methods. Their method delivers flicker-free depth video streams for future applications.

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Section: Rgbd Camera Prototypementioning

confidence: 99%

Section: Ground Truth Datamentioning

confidence: 99%

Spatio-Temporal Denoising for Depth Map Sequences

Hach

Seybold

2016

International Journal of Multimedia Data Engineering and Management

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“…All of these methods use custom rigs. The Arri company develops a prototype coupling professional video sensor with a ToF depth sensor [11]. Conversely, the method we propose can deal with every film camera.…”

Section: Technical Backgroundmentioning

confidence: 99%

A Game Engine as a Generic Platform for Real-Time Previz-on-Set in Cinema Visual Effects

Goussencourt

Dellac²,

Bertolino

2015

Advanced Concepts for Intelligent Vision Systems

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Abstract. We present a complete framework designed for film production requiring live (pre) visualization. This framework is based on a famous game engine, Unity R . Actually, game engines possess many advantages that can be directly exploited in real-time pre-vizualization, where real and virtual worlds have to be mixed. In the work presented here, all the steps are performed in Unity: from acquisition to rendering. To perform real-time compositing that takes into account occlusions that occur between real and virtual elements as well as to manage physical interactions of real characters towards virtual elements, we use a low resolution depth map sensor coupled to a high resolution film camera. The goal of our system is to give the film director's creativity a flexible and powerful tool on stage, long before post-production.

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“…Hach et al [ 19 ] present an RGB-D camera containing a combination of a ToF sensor and an RGB camera. Both systems are combined in the system without inclination to each other.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion of RGB and Depth Data with Image Enhancement

Wunsch,

Tenorio,

Anding

et al. 2024

J. Imaging

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Since 3D sensors became popular, imaged depth data are easier to obtain in the consumer sector. In applications such as defect localization on industrial objects or mass/volume estimation, precise depth data is important and, thus, benefits from the usage of multiple information sources. However, a combination of RGB images and depth images can not only improve our understanding of objects, capacitating one to gain more information about objects but also enhance data quality. Combining different camera systems using data fusion can enable higher quality data since disadvantages can be compensated. Data fusion itself consists of data preparation and data registration. A challenge in data fusion is the different resolutions of sensors. Therefore, up- and downsampling algorithms are needed. This paper compares multiple up- and downsampling methods, such as different direct interpolation methods, joint bilateral upsampling (JBU), and Markov random fields (MRFs), in terms of their potential to create RGB-D images and improve the quality of depth information. In contrast to the literature in which imaging systems are adjusted to acquire the data of the same section simultaneously, the laboratory setup in this study was based on conveyor-based optical sorting processes, and therefore, the data were acquired at different time periods and different spatial locations. Data assignment and data cropping were necessary. In order to evaluate the results, root mean square error (RMSE), signal-to-noise ratio (SNR), correlation (CORR), universal quality index (UQI), and the contour offset are monitored. With JBU outperforming the other upsampling methods, achieving a meanRMSE = 25.22, mean SNR = 32.80, mean CORR = 0.99, and mean UQI = 0.97.

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A novel RGB-Z camera for high-quality motion picture applications

Cited by 11 publications

References 9 publications

Spatio-Temporal Denoising for Depth Map Sequences

Spatio-Temporal Denoising for Depth Map Sequences

A Game Engine as a Generic Platform for Real-Time Previz-on-Set in Cinema Visual Effects

Data Fusion of RGB and Depth Data with Image Enhancement

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