Oct-trees and their use in representing three-dimensional objects

By Chris L. Jackins, S. Tanimoto. 1980

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Occupancy Networks: Learning 3D Reconstruction in Function Space (2018, methodology): “By incrementally building an octree [30,46,66,73], MISE enables us to extract high resolution meshes from the occupancy network without densely evaluating all points of a high-dimensional occupancy grid.”
Structure and motion from silhouettes (2001, methodology): “A simple technique for constructing an octree [63, 92] from the silhouettes is implemented.”
Emerging MPEG Standards for Point Cloud Compression (2019, background): “Resulting voxels may be mapped into an octree data structure to create a voxelized octree, which facilitates, in turn, the traversal, search, and access of the neighboring voxels [8], [9].”
Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images (2008, methodology): “To identify and process spine candidate voxels efficiently we utilize a data structure known as an octree [ 40 ], which recursively organizes objects distributed inside a 3D volume, allowing fast searching by spatial location (Fig. 1C).”