Internal:Hashing Meshes
Robust 3D mesh model hashing based on feature object pdf url
Abstract:
3D model hashing can be very useful for the authentication, indexing, copy detection, and watermarking of 3D content, in a manner similar to image hashing. 3D models can be easily modified by graphicsediting while preserving the geometric shape, and the modeling representations are not regular, unlike an image with a fixed pixel array. A 3D model must be authenticated, indexed, or watermarked while being robust against graphics attacks and irregular representations. For these purposes, this paper presents a 3D mesh model hashing based on object feature vectors with the robustness, security, and uniqueness. The proposed hashing groups the distances from feature objects with the highest surface area in a 3D model that consists of a number of objects, permutes indices of groups in feature objects, and generates a binary hash through the binarization of feature values that are calculated by two combinations of group values and a random key. The robustness of a hash can be improved by group coefficients that are obtained from the distribution of vertex distances in feature objects, and the security and uniqueness can be improved by both the permutation of groups, feature vectors, and random key. Experimental results verified that the proposed hashing is robust against various perceptual geometrical and topological attacks and has the security and uniqueness of a hash.
Parallel Compact Hash Algorithms for Computational Meshes pdf url
Abstract:
We employ compact hashing and the discrete properties of computational meshes to optimize spatial operations in scientific computing applications. Our target is to develop highly parallel compact hashing methods suitable for the fine-grained parallelism of GPU and MIC architectures that will scale to the next generation of computing systems. As a model, we apply spatial hashing methods to the problem of determining neighbor elements in adaptive mesh refinement (AMR) schemes. By applying memory savings techniques, we extend the perfect spatial hash algorithm to a compact hash by compressing the resulting sparse data structures. Using compact hashing and specific memory optimizations, we increase the range of problems that can benefit from our ideal $O(n)$ algorithms. The spatial hash methods are tested and compared across a variety of architectures on both a randomly generated sample mesh and an existing cell-based AMR shallow-water hydrodynamics scheme. We demonstrate consistent speed-up and increased performance across every device tested and explore the ubiquitous application of spatial hashing in scientific computing.