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The sparse cloud of 3D points as provided by the RDSLAM module represents visual key-points, which connect the augmented world with the physical world.
The result of markerless tracking provides the HMD camera location and orientation while the mapping result provides a representation of the physical world in form of a sparse cloud of 3D points.
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The fluctuations are best explained by sparse clouds covering about 1% of the moon, the team proposes in the 20 October issue of Science, from which methane must occasionally pour down.
Below the sparse clouds lay the verdant farms and ranches that hug the Saddle Road linking Hilo and Waimea.
Most modern packages allow for the construction of dense point clouds, either from the sparse point cloud and orientation (such as VisualSFM and PhotoScanPro), or using only the orientation parameters (iWitnessPro in combination with SURE).
The key idea is to use not only a sparse point cloud but also the generating process of the cloud on SLAM.
In this paper, a novel deep learning framework for 3D terrain scene recognition using 2D representation of sparse point cloud is proposed.
To do this they use a three-stage set of algorithms that begins by creating a "sparse point cloud" with a batch of photos, renders it as a denser image, capturing much of the original surface texture of the object, and then renders it in three dimensions.
First, an existing batch-based structural modeling is applied to a sparse point cloud that are incrementally updated on SLAM.
However, after the adjustment not only the camera parameters are known, also a sparse point cloud is created (Fig. 4).
After the image orientation, the position among images is known and a sparse point cloud with the tie-points position in 3D can be obtained.
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