Large scale and long standing simultaneous reconstruction and segmentation
Tateno K, Tombari F, Navab N (2017)
Publication Type: Journal article
Publication year: 2017
Journal
Book Volume: 157
Pages Range: 138-150
DOI: 10.1016/j.cviu.2016.05.013
Abstract
This work proposes a method to segment a 3D point cloud of a scene while simultaneously reconstructing it via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in an unified global model leveraging the camera pose estimated via SLAM. Differently from other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time and with a complexity that does not depend on the size of the global model. Moreover, we endow our system with two additional contributions: a loop closure approach and a failure recovery and re-localization approach, both specifically designed so to enforce global consistency between merged segments, thus making our system suitable for large scale and long standing reconstruction and segmentation. We validate our proposal against the state of the art in terms of computational efficiency and accuracy on several benchmark datasets, as well as by showing how our method enables real-time reconstruction and segmentation of diverse real indoor environments.
Involved external institutions
Germany (DE)How to cite
APA:
Tateno, K., Tombari, F., & Navab, N. (2017). Large scale and long standing simultaneous reconstruction and segmentation. Computer Vision and Image Understanding, 157, 138-150. https://doi.org/10.1016/j.cviu.2016.05.013
MLA:
Tateno, Keisuke, Federico Tombari, and Nassir Navab. "Large scale and long standing simultaneous reconstruction and segmentation." Computer Vision and Image Understanding 157 (2017): 138-150.
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