2015/01/01 by Samunda Perera, Nick Barnes, Xuming He +3 · 1 citation
Engineering · Computer Science · Earth and Planetary Sciences · #Robotics and Sensor-Based Localization #Advanced Vision and Imaging #3D Surveying and Cultural Heritage #Signed distance function #Artificial intelligence #Computer vision #Segmentation #Computer science #Voxel #Scale-space segmentation #Image segmentation #RGB color model #Motion (physics)
paper · doi:10.1109/wacv.2015.144
openalex publication_date 2015/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Truncated signed distance function (TSDF) based volumetric surface reconstructions of static environments can be readily acquired using recent RGB-D camera based mapping systems. If objects in the environment move then a previously obtained TSDF reconstruction is no longer current. Handling this problem requires segmenting moving objects from the reconstruction. To this end, we present a novel solution to the motion segmentation of TSDF volumes. The segmentation problem is cast as CRF-based MAP inference in the voxel space. We propose: a novel data term by solving sparse multi-body motion segmentation and computing likelihoods for each motion label in the RGB-D image space, and, a novel pairwise term based on gradients of the TSDF volume. Experimental evaluation shows that the proposed approach achieves successful segmentations on reconstructions acquired with Kinect Fusion. Unlike the existing solutions which only work if the objects move completely from their initially occupied spaces, the proposed method permits segmentation of objects when they start to move.