Joint de-noising and fusion of 2D video and depth map sequences sensed by low-powered tof range sensor

Georgiev, Mihail; Gotchev, Atanas; Hannuksela, Miska M.

doi:10.1109/icmew.2013.6618264

Cited by 7 publications

(4 citation statements)

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“…Previously, we have proposed techniques for depth resampling and 3D fusion for the case of an asymmetric non-confocal V+Z camera setup, where the depth is sensed in low-sensing conditions [43], [44], as well as techniques for near-lossless depth encoding [45], [46]. In the present work, we present a general framework, which addresses both cases.…”

Section: Relation With Previous Workmentioning

confidence: 99%

“…The efficiency of the proposed depth encoding approach relies on the ideal consistency between color and depth modalities. However, in real case of V+Z capture, depth maps can come with various artifacts caused by stereo-correspondence errors, low-resolution nonconfocal depth sensors along with projection misalignment and resampling ambiguities introduced by measurement errors [43], [44]. Examples of regions with such artifacts are given for a frame of ''Ballet'' data set in Figure 10 (c, d).…”

Section: ) Encoding Edge Outliers By ''Yield-flow'' Protocolmentioning

confidence: 99%

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A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

2020

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We present a general framework which can handle different processing stages of the three-dimensional (3D) scene representation referred to as ''view-plus-depth'' (V+Z). The main component of the framework is the relation between the depth map and the super-pixel segmentation of the color image. We propose a hierarchical super-pixel segmentation which keeps the same boundaries between hierarchical segmentation layers. Such segmentation allows for a corresponding depth segmentation, decimation and reconstruction with varying quality and is instrumental in tasks such as depth compression and 3D data fusion. For the latter we utilize a cross-modality reconstruction filter which is adaptive to the size of the refining super-pixel segments. We propose a novel depth encoding scheme, which includes specific arithmetic encoder and handles misalignment outliers. We demonstrate that our scheme is especially applicable for low bit-rate depth encoding and for fusing color and depth data, where the latter is noisy and with lower spatial resolution.

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Section: Relation With Previous Workmentioning

confidence: 99%

Section: ) Encoding Edge Outliers By ''Yield-flow'' Protocolmentioning

confidence: 99%

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

2020

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“…3, for varying integration times I T = [200,100, 50] μs in comparison to normal operating mode of I T =2000 μs. The figures illustrate the need of proper denoising procedure to be applied to the calculated distance maps in a post-capture stage -for more details see [5,6,7,8,9,10].…”

Section: A Low-sensing Environmentmentioning

confidence: 99%

“…In situations when the ToF device is forced to work in low-sensing mode, the amplitude is quite low and the distance measurements are more erroneous. Still, the reflectivity information can be utilized for effective post-capture denoising [7,8,9,10]. After it, the range data appears within the error limits as it was captured in normaloperating mode.…”

Section: Introductionmentioning

confidence: 99%