Distance Error Correction in Time-of-Flight Cameras Using Asynchronous Integration Time

Baek, Eu-Tteum; Yang, Hyung-Jeong; Kim, Soo-Hyung; Lee, Guee-Sang; Jeong, Hieyong

doi:10.3390/s20041156

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…(2) Black masking. Dark and matte surfaces absorb rather than reflecting radiations which strongly affected the depth map values [78]. We randomly mask the depth pixels whose RGB values are all in [0, 5] to directly mimic invalid depth values in dark regions.…”

Section: E Pseudo Depth Map For Trainingmentioning

confidence: 99%

RGB-Depth Fusion GAN for Indoor Depth Completion

Wang

Che

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.

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Section: E Pseudo Depth Map For Trainingmentioning

confidence: 99%

RGB-Depth Fusion GAN for Indoor Depth Completion

Wang

Che

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…As such, if the laser power of a ToF camera is increased to detect smaller particles (Raffel et al, 2018), care must be taken to ensure that the background or particles themselves do not over-saturate. Similarly, dark or matte surfaces are good absorbers and hence poor reflectors of light: a black surface will absorb most of the photons, leading to a low depth accuracy of black surfaces (Baek et al, 2020).…”

Section: Environmentmentioning

confidence: 99%

Three-Dimensional Particle Tracking Velocimetry using a Single Time-of-Flight Camera

Morris¹,

Goldman²,

Thurow³

2021

ISPIV21

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Time of Flight (ToF) cameras are a type of range-imaging camera that provides three-dimensional scene information from a single camera. This paper assesses the ability of ToF technology to be used for threedimensional particle tracking velocimetry (3D-PTV). Using a commercially available ToF camera various aspects of 3D-PTV are considered, including: minimum resolvable particle size, environmental factors (reflections and refractive index changes) and time resolution. Although it is found that an off-the-shelf ToF camera is not a viable alternative to traditional 3D-PTV measurement systems, basic 3D-PTV measurements are shown with large (6mm) particles in both air and water to demonstrate future potential use as this technology develops. A summary of necessary technological advances is also discussed.

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“…The direct-reflected light and the echoes are then acquired and separated through the decomposition of the measured radiance at the sensor [15]. The technique developed by Baek et al eliminates the depth error by employing a juxtaposition of three ToF cameras set to different integration times [16]. Although the results were successful, additional hardware and precise synchronizations for multiple ToF cameras are required.…”

Section: Introductionmentioning

confidence: 99%

Multipath Interference Suppression in Time-of-Flight Sensors by Exploiting the Amplitude Envelope of the Transmission Signal

2020

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Time-of-flight (ToF) cameras provide depth information and they have been applied in many areas. However, multipath interference (MpI) affects the depth measurements. This paper proposes an algorithm to suppress MpI during ToF camera depth measurements by exploiting the amplitude envelope of the camera's transmission wave. More precisely, it uses the fact that the amplitude envelope of the ToF camera's transmission wave is constant. The envelope of the reflected signal in the case of MpI deviates from that of the MpI-free envelope. By minimizing the deviation using an adaptive filter, the depth measurement error can be corrected. The method is applied at the sensor side of the ToF camera and lays the theoretical foundation for MpI suppression in ToF cameras. The simulation and experimental results demonstrate that the proposed algorithm is able to significantly improve the depth measurement accuracy.

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Distance Error Correction in Time-of-Flight Cameras Using Asynchronous Integration Time

Cited by 7 publications

References 23 publications

RGB-Depth Fusion GAN for Indoor Depth Completion

RGB-Depth Fusion GAN for Indoor Depth Completion

Three-Dimensional Particle Tracking Velocimetry using a Single Time-of-Flight Camera

Multipath Interference Suppression in Time-of-Flight Sensors by Exploiting the Amplitude Envelope of the Transmission Signal

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