Adaptive bilateral filtering for noise removal in depth upsampling

Yeo, Donghoon; Haq, Ehsan Ul; Kim, Jong-Dae; Baig, Mirza Waqar; Shin, Hyunchul

doi:10.1109/socdc.2010.5682980

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…The number of referenced color images is not restricted [121]. Yeo et al [107] analyzed another framework for upsampling the depth resolution, where the RGB-D camera system is shown in Fig. 11b.…”

Section: ) Resolution Improvementmentioning

confidence: 99%

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Chen

2019

IEEE Access

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Three-dimensional (3D) data acquisition and real-time processing is a critical issue in an artificial vision system. The developing time-of-flight (TOF) camera as a real-time vision sensor for obtaining depth images has now received wide attention, due to its great potential in many areas, such as 3D perception, computer vision, robot navigation, human-machine interaction, augmented reality, and so on. This paper survey advances in TOF imaging technology mainly from the last decade. We focus only on recent progress of overcoming limitations such as systematic errors, object boundary ambiguity, multipath error, phase wrapping, and motion blur, and address the theoretical principles and future research trends as well. INDEX TERMS Vision sensor, time of flight, depth image, sensing device, computer vision, 3D vision.

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Section: ) Resolution Improvementmentioning

confidence: 99%

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Chen

2019

IEEE Access

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“…To mitigate noise in depth maps, many different spatial filters are used [10,20,38,49,67,82,93]. These include the standard Gaussian and median filters [20].…”

Section: Spatial Filteringmentioning

confidence: 99%

“…Out of these different approaches, the bilateral filter is the most popular [48]. For many applications, images are also typically collected alongside depth maps, and many techniques [10,38,67,93] use them to help filter and enhance the depth maps. In our approach, we will show that the IR images that a ToF camera collects is sufficient to accurately filter depth maps.…”

Section: Spatial Filteringmentioning

confidence: 99%

Algorithms and Systems for Low Power Time-of-Flight Imaging

Noraky

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Depth sensing is useful for many emerging applications that range from augmented reality to robotic navigation. Time-of-flight (ToF) cameras are appealing depth sensors because they obtain dense depth maps with minimal latency. However, for mobile and embedded devices, ToF cameras, which obtain depth by emitting light and estimating its roundtrip time, can be power-hungry and limit the battery life of the underlying device. To reduce the power for depth sensing, we present algorithms to address two scenarios. For applications where RGB images are concurrently collected, we present algorithms that reduce the usage of the ToF camera and estimate new depth maps without illuminating the scene. We exploit the fact that many applications operate in nearly rigid environments, and our algorithms use the sparse correspondences across the consecutive RGB images to estimate the rigid motion and use it to obtain new depth maps. Our techniques can reduce the usage of the ToF camera by up to 85%, while still estimating new depth maps within 1% of the ground truth for rigid scenes and 1.74% for dynamic ones. When only the data from a ToF camera is used, we propose algorithms that reduce the overall amount of light that the ToF camera emits to obtain accurate depth maps. Our techniques use the rigid motions in the scene, which can be estimated using the infrared images that a ToF camera obtains, to temporally mitigate the impact of noise. We show that our approaches can reduce the amount of emitted light by up to 81% and the mean relative error of the depth maps by up to 64%. Our algorithms are all computationally efficient and can obtain dense depth maps at up to real-time on standard and embedded computing platforms. Compared to applications that just use the ToF camera and incur the cost of higher sensor power and to those that estimate depth entirely using RGB images, which are inaccurate and have high latency, our algorithms enable energy-efficient, accurate, and low latency depth sensing for many emerging applications.

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“…From the remaining noise variance information with respect to the pixel location and the distance measurement after sensor calibration, we can build a depth noise variance map. If we assume a zero-mean Gaussian noise, an adaptive depth denoising algorithm which uses the noise variance information such as [41] may be a strong application.…”

Section: Applicationsmentioning

confidence: 99%

Time-of-Flight Sensor Calibration for a Color and Depth Camera Pair

Jung

Lee

Jeong

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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We present a calibration method of a time-of-flight (ToF) sensor and a color camera pair to align the 3D measurements with the color image correctly. We have designed a 2.5D pattern board with irregularly placed holes to be accurately detected from low resolution depth images of a ToF camera as well as from high resolution color images. In order to improve the accuracy of the 3D measurements of a ToF camera, we propose to perform ray correction and range bias correction. We reset the transformation of the ToF sensor which transforms the radial distance into the scene depth in Cartesian coordinate through ray correction. Then we capture a planar scene from different depths to correct the distance error that is shown to be dependent not only on the distance but also on the pixel location. The range error profiles along the calibrated distance are classified according to their wiggling shapes and each cluster of profiles with similar shape are separately estimated using a B-spline function. The standard deviation of the remaining random noise is recorded as an uncertainty information of distance measurements. We show the performance of our calibration method quantitatively and qualitatively on various datasets, and validate the impact of our method by demonstrating an RGB-D shape refinement application.

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Adaptive bilateral filtering for noise removal in depth upsampling

Cited by 14 publications

References 9 publications

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Algorithms and Systems for Low Power Time-of-Flight Imaging

Time-of-Flight Sensor Calibration for a Color and Depth Camera Pair

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