Denoising Strategies for Time-of-Flight Data

Lenzen, Frank; Kim, Kwang In; Schäfer, Helmut; Nair, Rahul; Meister, Sarah; Becker, Florian; Garbe, Christoph S.; Theobalt, Christian

doi:10.1007/978-3-642-44964-2_2

Cited by 20 publications

(17 citation statements)

References 39 publications

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“…Finally, some nonsystematic depth deformations which rather correspond to noise are also reported. One should notice the existence of denoising and filtering methods for their reduction [43], as well as methods related to the multiple returns issue [44].…”

Section: Error Sources and Calibration Methodsmentioning

confidence: 99%

Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling

et al. 2015

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Abstract:In the last decade, RGB-D cameras -also called range imaging cameras -have known a permanent evolution. Because of their limited cost and their ability to measure distances at a high frame rate, such sensors are especially appreciated for applications in robotics or computer vision. The Kinect v1 (Microsoft) release in November 2010 promoted the use of RGB-D cameras, so that a second version of the sensor arrived on the market in July 2014. Since it is possible to obtain point clouds of an observed scene with a high frequency, one could imagine applying this type of sensors to answer to the need for 3D acquisition. However, due to the technology involved, some questions have to be considered such as, for example, the suitability and accuracy of RGB-D cameras for close range 3D modeling. In that way, the quality of the acquired data represents a major axis. In this paper, the use of a recent Kinect v2 sensor to reconstruct small objects in three dimensions has been investigated. To achieve this goal, a survey of the sensor characteristics as well as a calibration approach are relevant. After an accuracy assessment of the produced models, the benefits and drawbacks of Kinect v2 compared to the first version of the sensor and then to photogrammetry are discussed.

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Section: Error Sources and Calibration Methodsmentioning

confidence: 99%

Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling

et al. 2015

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“…4) RFD: The random forest regression-based distortion removal algorithm [30]. 5) TV: The total variation-based algorithm [24]. 6) PD: The shape-prior based patch algorithm [1].…”

Section: A Baseline Methodsmentioning

confidence: 99%

“…Comparison with the RFD justified the use of NNs for removing multipath distortion. For the TV [24], we used L1 norm for the regularization term in the objective function to preserve structure of the range image. The TV objective function was optimized by a primal-dual approach [6].…”

Section: A Baseline Methodsmentioning

confidence: 99%

“…Lenzen et al [24] propose a Total Variation (TV) method for ToF range image denoising. It estimates the true range image by minimizing a predefined objective function that consists of data-fidelity terms and regularization terms.…”

Section: A Related Workmentioning

confidence: 99%

“…We varied the threshold and plotted the performance curve. We computed the results for the , [30], (e) PD [1], (f) TV [24], (g) RFD [30], and (h) the proposed algorithm. (Due to the space limitation, the visual result of WMF is not displayed in this Figure. Figure 6.…”

Section: B Performance Metricmentioning

confidence: 99%

See 2 more Smart Citations

Learning to remove multipath distortions in Time-of-Flight range images for a robotic arm setup

Son

Taguchi

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Range images captured by Time-of-Flight (ToF) cameras are corrupted with multipath distortions due to interaction between modulated light signals and scenes. The interaction is often complicated, which makes a model-based solution elusive. We propose a learning-based approach for removing the multipath distortions for a ToF camera in a robotic arm setup. Our approach is based on deep learning. We use the robotic arm to automatically collect a large amount of ToF range images containing various multipath distortions. The training images are automatically labeled by leveraging a high precision structured light sensor available only in the training time. In the test time, we apply the learned model to remove the multipath distortions. This allows our robotic arm setup to enjoy the speed and compact form of the ToF camera without compromising with its range measurement errors. We conduct extensive experimental validations and compare the proposed method to several baseline algorithms. The experiment results show that our method achieves 55% error reduction in range estimation and largely outperforms the baseline algorithms.

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Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

Guo

Frosio

Gallo

et al. 2018

Computer Vision – ECCV 2018

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Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.

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Denoising Strategies for Time-of-Flight Data

Cited by 20 publications

References 39 publications

Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling

Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling

Learning to remove multipath distortions in Time-of-Flight range images for a robotic arm setup

Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset

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