Fully-Convolutional Point Networks for Large-Scale Point Clouds
Rethage D, Wald J, Sturm J, Navab N, Tombari F (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11208 LNCS
Pages Range: 625-640
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783030012243
DOI: 10.1007/978-3-030-01225-0_37
Abstract
This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data. One striking characteristic of our approach is its ability to process unorganized 3D representations such as point clouds as input, then transforming them internally to ordered structures to be processed via 3D convolutions. In contrast to conventional approaches that maintain either unorganized or organized representations, from input to output, our approach has the advantage of operating on memory efficient input data representations while at the same time exploiting the natural structure of convolutional operations to avoid the redundant computing and storing of spatial information in the network. The network eliminates the need to pre- or post process the raw sensor data. This, together with the fully-convolutional nature of the network, makes it an end-to-end method able to process point clouds of huge spaces or even entire rooms with up to 200k points at once. Another advantage is that our network can produce either an ordered output or map predictions directly onto the input cloud, thus making it suitable as a general-purpose point cloud descriptor applicable to many 3D tasks. We demonstrate our network’s ability to effectively learn both low-level features as well as complex compositional relationships by evaluating it on benchmark datasets for semantic voxel segmentation, semantic part segmentation and 3D scene captioning.
Involved external institutions
Germany (DE)
United States (USA) (US)How to cite
APA:
Rethage, D., Wald, J., Sturm, J., Navab, N., & Tombari, F. (2018). Fully-Convolutional Point Networks for Large-Scale Point Clouds. In Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 625-640). Munich, DEU: Springer Verlag.
MLA:
Rethage, Dario, et al. "Fully-Convolutional Point Networks for Large-Scale Point Clouds." Proceedings of the 15th European Conference on Computer Vision, ECCV 2018, Munich, DEU Ed. Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert, Springer Verlag, 2018. 625-640.
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