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The ModelCamera implements an interactive modeling pipeline based on real-time dense color and sparse depth.
Still, while the pattern of camera's projector can be visible, in cloudy conditions for instance, the resulting sparse depth map is exploitable and allows good predictions.
Thus, this method can be applied to real-time applications that require a very sparse depth map [3], for example in image registration applications.
We propose an efficient approach for building compact, edge-preserving, view-centric triangle meshes from either dense or sparse depth data, with a focus on modeling architecture in large-scale urban scenes.
These cases presented similarities, namely the presence of the stairs on the left hand side of the images or a highly sparse depth map due to either a lack of texture or motion blur (Fig. 12).
While the idea of merging sparse depth data, obtained by the stereo-vision, with SfS output data is not new [15, 16], this paper proposes, for the first time, a comprehensive system hardware architecture implementing the two orthogonal approaches.
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Instead, the SNS method requires only sparse read depth (as few as 2 million uniquely mapped 76 bp single-end reads) evenly distributed along the genome.
The annotations can be sparse, as the depth map is generated by a diffusion process, which is guided by image features.
However, descriptions within historical documents capable of indicating the flow depth are sparse.
Therefore, for large scale models applied in data sparse areas, calibrating channel depth and/or shape may be preferable to assuming a rectangular geometry and calibrating friction alone.
The academic literature is sparse on comprehensive in-depth studies about the intricate processes involved in shaping and managing technology development for the masses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com