PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

By Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, Chiew-Lan Tai. Computer Vision and Pattern Recognition, 2021

370 citations recorded by the source corpus.

Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning techniques in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand- crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors. [code release]

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/0cd8108c6d87df71c0722f380dff90d6188678fe

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. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

In Environmental Engineering

1. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

In Geology

1. Zheng Qin
2. Changjian Wang
3. Kaiping Xu
4. Yulan Guo
5. Yuxing Peng

Citations in the corpus

• Geometric Transformer for Fast and Robust Point Cloud Registration (2022, methodology): “As traiditional robust estimators such as RANSAC suffer from slow convergence and instability in case of high outlier ratio, deep robust estimators [3, 6, 20] have been proposed as the alternatives for them.”