Point cloud denoisingColor denoisingConvex optimizationTikhonov regularizationTotal variationGraph signal processingA point cloud is a representation of a 3D scene as a discrete collection of geometry plus other
This means that the method is insensitive to the dimension of the input space, and it can be applied to a generic point cloud \(\{\bar{\varvec{u}}^{i}\}_{i=1}^{\bar{N}}\) embedded in \(\mathbb {R}^D\) that has to be transformed based on a few more accurate, or ...
to be used as reference for the synthesis of additional virtual views at the client. This in-network synthesis leads to better viewpoint sampling given a bandwidth constraint compared to simple selection of camera views, but it may however carry a distortion penalty in the cloudlet-synthesized ref...
In practical applications, the manifoldMis not directly observed and instead, a sampled point cloudXis provided (\(X\subset i(M)\subset {\mathbb {R}}^m\)). The remainder of this section focuses on a graph Laplacian-based discretization of Eq.7. A weighted graph\(G=(X,E,W)\)consists...
Additionally, subsequent research will consider exploring the integration of some external factors into the RT-GCN, such as weather, point of interest (POI), periodicity, and events, which could potentially contribute to the development of more efficient transportation systems. Lastly, RT-GCN could ...