You are cordially invited to attend our CG Colloquium on Thursday, April 18, 2019, 15:45-17:45h, at Pulse Hall 4 .
The program features the following three speakers:
Remi van der Laan
Title: Enhancing Compression of the Sparse Voxel Directed Acyclic Graph
Abstract: Rendering massive scenes in real time represented as voxels has emerged as an attractive alternative compared to the traditional rendering pipeline. This has been achieved through the development of data structures that can efficiently store the scene data while also being inexpensive to traverse. The Sparse Voxel Directed Acyclic Graph is such a data structure, which losslessly compresses geometry by exploiting the spatial coherence in the scene. We attempt enhance the effectiveness of this compression through modifications to the construction of the graph and investigate possibility of applying lossy compression techniques.
Hao Ming Ye
Title: not available at the moment
Abstract: not available at the moment
Title: Graph Convolutional Networks for Learning on Point Clouds
Abstract: In the past decade, Convolutional Neural Networks (CNNs) have achieved incredible results. A recent development in the deep learning community is the attempt to generalise the advantages of CNNs from a Euclidean domain to non-Euclidean domains, like graphs and manifolds. Some examples of graph data are social networks, regulatory networks, functional networks, and 3D shapes. We attempt to get an understanding of the methods currently available and aim to improve on the current methodology for learning on point clouds. A tentative conclusion is that the conceptually simple Graph Convolutional Network by Kipf and Welling could be improved for manifolds through the incorporation of the connection Laplacian from vector diffusion maps.