The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Open accessAbstract
The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and an-notating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.
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Cited by
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP
Zhang, J., Dong, R., & Ma, K. (2023) · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00219 · Cited by 62
Metadata
- Authors
- Hou, J., Graham, B., Niesner, M., & Xie, S.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 15582-15592
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3166573884
- Cited by
- 283
- References
- 72
- FWCI
- 40.48
- Type
- conference paper
- Language
- en
- Open access
- green
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