2026-04-19T19:14:24Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1197422026-02-07T10:40:18Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Lin, Xiao author Casas Pla, Josep Ramon author Pardàs Feliu, Montse author 2017 Traditional image segmentation methods working with low level image features are usually difficult to adapt to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research field. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point cloud data obtained from consumer depth sensors has been exploited to tackle many computer vision problems due to its richer information about the geometry of 3D scenes compared to 2D images. Meanwhile, new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. In this paper, we present a novel point cloud segmentation approach for segmenting interacting objects in a stream of point clouds by exploiting spatio-temporal coherence. We pose the problem as an energy minimization task in a fully connected conditional random field with the energy function defined based on both current and previous information. We compare different methods and prove the improved segmentation performance and robustness of the proposed approach in sequences with over 2k frames. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal Signal processing Computer vision Image segmentation Image sequences Minimisation Object recognition Tractament del senyal 3D point cloud segmentation using a fully connected conditional random field