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Unsupervised feature learning for 3D scene labeling
Abstract
This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.
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“Similarly, Lai et al. [148] build a 3D map from RGB-D sequences to then carry out an offline object classification.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [148]
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Metadata
- Authors
- Lai, K., Bo, L., & Fox, D.
- Affiliations
- University of Washington (US)
- Amazon (United States) (US)
- Venue
- 2014 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 3050-3057
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2073700113
- Cited by
- 317
- References
- 41
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 13 · 2023: 20 · 2022: 16 · 2021: 34 · 2020: 26 · 2019: 33 · 2018: 40 · 2017: 43 · 2016: 44 · 2015: 21 · 2014: 4
- FWCI
- 70.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised feature learning, 3D point clouds, RGB-D, virtual scene generation, CAD models, indoor scene understanding
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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