Spatial-Depth Super Resolution for Range Images

By Qingxiong Yang, Ruigang Yang, James Davis, David Nistér. 2007

848 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Instrumentation

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Citations in the corpus

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, background): “ its low-resolution (LR) counterpart is referred to as super-resolution (SR). SR received substantial attention from within the computer vision research community and has a wide range of applications [63,71,43]. 4 SRGAN (proposed) original Figure 1: Super-resolved image (left) is almost indistinguis”
High quality depth map upsampling for 3D-TOF cameras (2011, methodology): “Representative image fusion approaches include [6, 19, 4, 7].”
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation (2013, methodology): “Existing approaches , such as [3] [24], calculate this depth upsampling by a bilateral filtering.”
Accurate depth map estimation from a lenslet light field camera (2015, neutral): “[28] .”
Depth Map Super-Resolution by Deep Multi-Scale Guidance (2016, methodology): “used the joint bilateral filter [1] to weight the degree of smoothing in each depth patch by considering the color similarity between the center pixel and its neighborhood [13].”
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction (2018, background): “Many algorithms therefore start with discrete matching and then reﬁne these matches by ﬁtting a local curve such as a parabolic ﬁt to the cost function between the discrete disparity candidates (see e.g. [59,39]).”
Super-resolution: a comprehensive survey (2014, background): “However, in [200], [250], [296], [324], [354], [388], [421], [578] 3D to 3D SR algorithms have been developed to generate HR 3D images from”
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model (2014, methodology): “Joint bilateral ﬁltering [12] and its variations are readily available tools for depth recovery using high quality auxiliary color images [14]–[16].”
Fusion of time-of-flight depth and stereo for high accuracy depth maps (2008, background): “Therefore several super resolution methods [10] [26] are reported to enhance the resolution of depth images.”
Sparsity Invariant CNNs (2017, background): “A popular choice for guided upsampling is guided bilateral ﬁltering [2, 7, 34, 42, 68].”
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow (2014, positive): “produce subpixel accuracy with a more efficient technique – paraboloid fitting, which is a 2D extension from the 1D parabola fitting – a commonly adopted technique in stereo matching [30].”
Real-time O(1) bilateral filtering (2009, background): “Originally introduced by Tomasi and Manduchi [2], bilateral filters are edge preserving operators that have found widespread use in many computer vision and graphics tasks like denoising [3, 4, 5, 6, 7], texture editing and relighting [8], tone management [9, 10], demosaicking [11], stylization [12]”
Vision Enhancement in Homogeneous and Heterogeneous Fog (2012, methodology): “These disparity maps being of sufficient quality, we performed a cross/ joint bilateral filtering to fill in the remaining holes using the original image as an interpolation guide [20].”
Learning Guided Convolutional Network for Depth Completion (2019, background): “…information, edge information, or surface information, etc. Earlier works mainly address depth super-resolution with bilateral ﬁltering [16], or global energy minimization [26]–[28], where the depth completion is guided by image [16], [26]–[28], semantic segmentation [29] or edge information [30].”
A non-local cost aggregation method for stereo matching (2012, neutral): “[5] used bilateral filter for depth superresolution.”
Deep Joint Image Filtering (2016, background): “The bilateral filters [10,1,4,14,9,15] and guided filters [6] are representative algorithms in this class.”
PatchMatch Stereo - Stereo Matching with Slanted Support Windows (2011, background): “…simplest form, this is accomplished by extending the label space, i.e., some fractional disparity values (half- or quarter-pixel) are considered in addition to the whole-valued ones (e.g., [6]).2 Instead of adding additional fronto-parallel planes to the label space, one can add slanted ones [5].”