5 accepted papers
Depth completion, the process of generating dense depth maps from sparse or incomplete data, is inherently challenging due to the variability in sensor types, environmental conditions, and the differences between data modalities. In this paper, we propose a novel framework for depth completion. Firs
Point cloud segmentation plays a crucial role in extracting unique attributes and separating various objects, thereby enabling semantic comprehension and analysis. In this paper, we introduce a novel point cloud segmentation approach based on Diffusion Probabilistic Network (DDPM). The proposed mode…
This paper introduces a novel joint spatio-temporal filtering algorithm and investigate its impact on data alignment in transfer learning (TL) for motion imagery (MI) tasks to deal with the variability in subjects, trials, or tasks. While spatial filtering is an integral part of the common spatial p…
Common spatial pattern (CSP) is widely used in motor imagery classification tasks. Classical CSP depends only on spatial filters. To improve its performance, a novel and efficient spatio-temporal filtering strategy is proposed in this paper to extract discriminative features. Common temporal filters…
Because the spectrum folding phenomenon affects the down-sampling of graph signals, both critically sampled and oversampled graph filter banks with down-sampling operations can only be applied to bipartite graphs. However, general graph signals may not reside on bipartite graph structures. In this p…