Increasing depth lateral resolution based on sensor fusion

Hannemann, W.; Linarth, A.; Liu, B.; Kókai, Gabriella; Jesorsky, Oliver

doi:10.1504/ijista.2008.021302

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…A layered Markov Random Field (MRF) model in [24] with the purpose to correlate a continuous smooth surface to the given samples of depth data. The MRF formalization have been further advanced in [25], [26] and [27]. In [28], the problem has been cast as in a dissipated heat anisotropic diffusion network, where the heat sources are the available data samples.…”

Section: B 3d Fusion Of Asymmetric View-plus-depth Datamentioning

confidence: 99%

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: B 3d Fusion Of Asymmetric View-plus-depth Datamentioning

confidence: 99%

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

2020

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“…This method is able to improve the quality of depth maps, but tends to over-smooth the depth images. To reduce over-smoothing, Hannemann et al [23] have incorporated the amplitude values generated by Time-of-Flight camera into an MRF model to improve the quality of interpolation. The amplitude can be evaluated as a confidence measurement for the depth values.…”

Section: A Depth Image Recoverymentioning

confidence: 99%

Global Auto-Regressive Depth Recovery via Iterative Non-Local Filtering

Yang

Frossard

2019

IEEE Trans. on Broadcast.

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Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we present a novel framework for obtaining high-quality depth images and multi-view depth videos from simple acquisition systems. We first propose a single depth image recovery algorithm based on auto-regressive (AR) correlations. A fixedpoint iteration algorithm under the global AR modeling is derived to efficiently solve the large-scale quadratic programming. Each iteration is equivalent to a nonlocal filtering process with a residue feedback. Then, we extend our framework to an ARbased multi-view depth video recovery framework, where each depth map is recovered from low-quality measurements with the help of the corresponding color image, depth maps from neighboring views, and depth maps of temporally adjacent frames. AR coefficients on nonlocal spatiotemporal neighborhoods in the algorithm are designed to improve the recovery performance. We further discuss the connections between our model and other methods like graph-based tools, and demonstrate that our algorithms enjoy the advantages of both global and local methods. Experimental results on both the Middleburry datasets and other captured datasets finally show that our method is able to improve the performances of depth images and multi-view depth videos recovery compared with state-of-the-art approaches. Index Terms-Depth recovery, multi-view depth video, autoregressive model, nonlocal correlation, iterative filtering. I. INTRODUCTION 3 D IMAGING applications have rapidly gained interest in recent years, as they promise to enhance the visual experience by extending the conventional 2D video to a more immersive experience. However, the technologies constituting 3D imaging systems are not fully mature yet to

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“…Diebel and Thrun [22] presented a Markov Random Field (MRF) model to integrate such high-resolution color images in order to smooth the depth data and to enhance the lateral resolution. This approach was also applied to image and depth data from combined time-of-flight and color camera systems [23,24]. Kopf et al [25] upsampled data in different applications using a joint bilateral filter with a high resolution ''guidance image" as prior.…”

Section: Super-resolutionmentioning

confidence: 99%