Direct visibility of point sets

By Sagi Katz, Ayellet Tal, Ronen Basri. ACM Transactions on Graphics, 2007

509 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Geology

View Paper: https://www.semanticscholar.org/paper/c70e44131988a28393fe6b324263edc9a8a878b1

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Geology

Citations in the corpus

Performance Evaluation of 3D Keypoint Detectors (2012, methodology): “point of view the algorithm described in Katz et al. (2007) on the 3D scene built by randomly rotating and translating the selected 3D models. Both synthetic datasets have been made publicly available. Datasets can also be categorized according to the application scenario they address. In one of the”
Learning a non-linear knowledge transfer model for cross-view action recognition (2015, methodology): “The remaining 3D points are projected orthographically to the x y plane as shown in Fig.”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 3d Face Reconstruction from a Single Image Using a Single Reference Face Shape (unknown): “To remove the occluded points we use the approximate z-buffer technique for point sets by [18].”
Learning a Deep Model for Human Action Recognition from Novel Viewpoints (2016, methodology): “Then, the hidden point removal technique [62] is applied on the remaining 3D points.”
A Survey of Surface Reconstruction from Point Clouds (2017, unknown): “If both are occluded or visible via [KTB07], this indicates an inconsistency.”
3D Action Recognition from Novel Viewpoints (2016, methodology): “First, we perform back-face culling by removing points whose normals face away from the camera and then perform hidden point removal [22] on the remaining points.”
Exploring photobios (2011, methodology): “Given the estimated pose we transform the template shape to the orientation of the face in the image and warp the image to a frontal pose using point-set z-buffering [Katz et al. 2007] to account for occlusions.”
3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding (2014, unknown): “First we perform back-face culling, and on the remaining points we use a freely available off-the-shelf implementation of the method by Katz et al. 1 [11].”
Learned Multi-patch Similarity (2017, methodology): “The ground truth 3D point cloud is processed using the visibility check of [15].”