Lin X, Casas J, Pardàs M. 3D Point Cloud Segmentation Using a Fully Connected Conditional Random Field. In The 25th European Signal Processing Conference (EUSIPCO 2017). Kos island, Greece: Eurasip/IEEE; 2017.  (2.34 MB)


Traditional image segmentation methods working with low level image features are usually difficult to be adapted to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research filed. 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 better segmentation performance and robustness of the proposed approach in sequences with over 2k frames.