The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Shape-based recognition of 3D point clouds in urban environments
Abstract
This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potential object locations (with hierarchical clustering). Then, we segment points near those locations into foreground and background sets (with a graph-cut algorithm). Next, we build a feature vector for each point cluster (based on both its shape and its context). Finally, we label the feature vectors using a classifier trained on a set of manually labeled objects. The paper presents several alternative methods for each step. We quantitatively evaluate the system and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest. Then, we use this truth data as a training set to recognize objects amidst approximately 1 billion points of the remainder of the Ottawa scan.
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Cites
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Cited by
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Zhu, X., Ma, Y., Wang, T., Xu, Y., Shi, J., & Lin, D. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58595-2_35 · Cited by 119
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105
Metadata
- Authors
- Golovinskiy, A., Kim, V. G., & Funkhouser, T.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 2154-2161
- OpenAlex
- W2102402541
- Cited by
- 415
- References
- 1
- FWCI
- 52.52
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Golovinskiy, A., Kim, V. G., & Funkhouser, T. (2009). Shape-based recognition of 3D point clouds in urban environments. 2009 IEEE 12th International Conference on Computer Vision. https://doi.org/10.1109/iccv.2009.5459471
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