An Offsetting Framework of Triangular Models for 3D Printing

An efficient and intersection-free model offsetting framework is introduced in this paper to generate shell models for 3D printing. The basic concept of the framework is to offset vertices of the input mesh to obtain an approximate discrete signed distance field for reconstructing the offsetting mesh. The framework first offsets vertices of the mesh by a given distance along their normal directly. These vertices are then adjusted or discarded according to the given offsetting distance to form an approximate discrete signed distance field using a binary space partition (BSP) tree. These reserved vertices are finally reconstructed using Poisson reconstruction algorithms to form the inner surface of the shell model. Results of the framework are intersection and non-manifold free for an arbitrary distance. It also allows different parts of a model for different offsetting distances from user interactions. Several examples are given to demonstrate that the framework is effective and robust for 3D printing.

Steganography: Securing Message in wireless network

Steganography is the process of hiding a secret message with in a cover medium. However eavesdropper may guess the embedding algorithm like least significant bit (LSB) replacement of Chan et al, 2004; Wang et al, 2001; Wu et al, 2005, LSB matching of Mielikainen, 2006, addition and/or subtraction of Andead wastfield, 2001; F. Huang et al in 2011, Exploiting Modification Direction by Zhang and Wang, 2006, Binary Space Partition by Tsai and Wang, 2007, modulus function of Chin et al, 2011 and thus can apply the respective extraction method to detect the secret message. So challenges lies in the methodologies of embedding message. Capacity, security and robustness are the services to be demanded by users. Again the true-positive rate of secret message detection by eavesdropper should be lessened by applying firm technique. Thirdly operating domain should be less sensitive to the noise, margin level of losses or alteration of data while communicating through unguided medium like wireless network, sensor network and cellular network. This paper will briefly discuss the steganographic methods and their experimental results explained in the survey paper of Niels Provos and Peter Honeyman to hide and seek message. Finally the proposed results and the directions for future works are addressed.

SEPARABILITY OF POINT SETS BY k-LEVEL LINEAR CLASSIFICATION TREES

Let R and B be sets of red and blue points in the plane in general position. We study the problem of computing a k-level binary space partition (BSP) tree to classify/separate R and B, such that the tree defines a linear decision at each internal node and each leaf of the tree corresponds to a (convex) cell of the partition that contains only red or only blue points. Specifically, we show that a 2-level tree can be computed, if one exists, in time O(n2). We show that a minimum-level (3 ≤ k ≤ log n) tree can be computed in time nO( log n). In the special case of axis-parallel partitions, we show that 2-level and 3-level trees can be computed in time O(n), while a minimum-level tree can be computed in time O(n5).

Smart Nearest Neighbors

This document presents an implementation of two algorithms, Voronoi Neighbors and Binary Space Partition (BSP) Neighbors. These algorithms find neighbors of a point in a point set that are somehow better'' than aK nearest neighbors’’ or a ``all neighbors within a radius’’ query. This type of nearest neighbor query is more computationally expensive, but results in set of neighbors with more desirable properties. The BSP Neighbors search ensures that there is less local duplication, while the Voronoi Neighbors search ensures that the spatial arrangement of the neighbors is as uniform as possible.These algorithms are explained in ``Point Primitives for Interactive Modeling and Processing of 3D Geometry’’.The code is available here: https://github.com/daviddoria/SmartNearestNeighbors