2017 International Conference on 3D Vision (3DV) (2017)
Oct 10, 2017 to Oct 12, 2017
In recent years, major advances have been made in 3D scene reconstruction. However, much less progress has been made for objects, which can exhibit far fewer unambiguous geometric/texture cues than a full scene, and thus are much harder to track against. In this work we present a novel probabilistic object reconstruction framework that simultaneously allows for online, implicit deformation of the objects surface to reduce tracking drift and handle loop closure events. Coupled with our probabilistic formulation is the use of a multi subsegment representation of the object, used to enforce global consistency, with segmentation of the object built in to the formulation. Finally, we employ a CRF framework to refine the overall segmentation, defined by a probability field over the object. We present compelling results over the current state-of-the-art object reconstruction work and demonstrate robustness and consistency w.r.t. established dense SLAM frameworks.
image reconstruction, image segmentation, image sequences, image texture, object tracking, probability, robot vision, SLAM (robots)
J. Hunt, V. Prisacariu, S. Golodetz, T. Cavallari, N. Lord and P. Torr, "Probabilistic Object Reconstruction with Online Global Model Correction," 2017 International Conference on 3D Vision (3DV), Qingdao, China, 2018, pp. 291-300.