SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

By Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo. Computer Vision and Pattern Recognition, 2020

402 citations recorded by the source corpus.

Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.

Subfields: Aerospace Engineering, Computational Mechanics, Geology

View Paper: https://www.semanticscholar.org/paper/6c0f9090fa119009047e49e5530cae28f0dd2c7e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fu Li
2. Benjamin Busam
3. Mahdi Saleh
4. Zixin Luo
5. Lei Zhou
6. Xuyang Bai
7. Hongbo Fu
8. Chiew-Lan Tai

In Computational Mechanics

1. Fu Li
2. Benjamin Busam
3. Mahdi Saleh
4. Zixin Luo
5. Xuyang Bai
6. Lei Zhou
7. Chiew-Lan Tai
8. Hongbo Fu

In Geology

1. Fu Li
2. Benjamin Busam
3. Mahdi Saleh
4. Xuyang Bai
5. Zixin Luo
6. Lei Zhou
7. Hongbo Fu
8. Chiew-Lan Tai

Citations in the corpus

• PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency (2021, methodology): “Recently, learningbased algorithms have been proposed to replace the individual components in the classical registration pipeline, including keypoint detection [4, 38, 32] and feature description [21, 22, 23, 51, 4, 18, 27, 30, 2].”
• Geometric Transformer for Fast and Robust Point Cloud Registration (2022, methodology): “Most correspondence-based methods rely on keypoint detection [1,4,7,15].”
• CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (2021, methodology): “SpinNet [30] utilizes LRF for patch alignment and 3D cylindrical convolution layers for feature extraction, achieving the best generalization ability to unseen datasets.”
• Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (2022, neutral): “Although remarkable progress has been achieved recently [2, 3, 7, 11, 24, 39, 53, 54], these”