Cascading Feature Extraction for Fast Point Cloud Registration
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Abstract (arXiv)
We propose a method for speeding up a 3D point cloud registration through a cascading feature extraction. The current approach with the highest accuracy is realized by iteratively executing feature extraction and registration using deep features. However, iterative feature extraction takes time. Our proposed method significantly reduces the computational cost using cascading shallow layers. Our idea is to omit redundant computations that do not always contribute to the final accuracy. The proposed approach is approximately three times faster than the existing methods without a loss of accuracy.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 23 Oct 2021.
Builds on
8 of 17 references.
References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 321
- 2020RPM-Net: Robust Point Matching using Learned Featurescited by 182
- 2018Learning Latent Permutations with Gumbel-Sinkhorn Networkscited by 175
- 2020Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondencescited by 109
- 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 97
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Citing papers per year: 2024: 1.
1 paper.
- 2024Unsupervised Point Cloud Registration with Self-Distillation11 Sep 2024