Rigid registration is a transformation estimation problem between two point clouds. The two point clouds captured may partially overlap owing to different viewpoints and acquisition times. Some previous correspondence matching based methods utilize an encoder-decoder network to carry out partial-to-partial registration task and adopt a skip-connection structure to convey information between the encoder and decoder. However, equally revisiting them with skip-connection may introduce the information redundancy, and limit the feature learning ability of the entire network. To address these problems, we propose a skip-attention based correspondence filtering network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SACF-Net ) for point cloud registration. A novel feature interaction mechanism is designed to utilize both low-level geometric information and high-level context-aware information to enhance the original pointwise matching map. Additionally, a skip-attention based correspondence filtering method is proposed to selectively revisits features in the encoder at different resolutions, allowing the decoder to extract high-quality correspondences within overlapping regions. We conduct comprehensive experiments on indoor and outdoor scene datasets, and the results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SACF-Net yields unprecedented performance improvements.

SACF-Net: Skip-attention Based Correspondence Filtering Network for Point Cloud Registration

We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the cross-point-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark. Code and data are available at https://github.com/rabbityl/lepard. 

/pdf/lepard-learning-partial-point-cloud-matching-in-rigid-and-2syl24ei.pdf

Lepard: Learning partial point cloud matching in rigid and deformable scenes

Point cloud registration is a key problem in the application of computer vision to robotics, autopilot and other fields. However, because the object is partially covered up or the resolution of 3D scanners is different, point clouds collected by the same sense may be inconsistent and even incomplete. Inspired by the recently proposed learning-based approaches, we propose Inliers Estimation Network (INENet) which includes a self-designed threshold prediction network and a probability estimation network with adaptive similarity mutual attention to help to find the overlapping area of the point clouds. In order to solve the above problems, we divide the partially overlapping point cloud registration task into two sub-tasks: overlapping areas detection and registration. The threshold prediction network can automatically calculate the threshold according to the input point clouds, and then the probability estimation network estimates the overlapping points by using threshold. The advantages of the proposed approach include: (1) threshold prediction network avoids bias and the complexity of manually adjusting the threshold. (2) Probability estimation network with similarity matrix can deeply fuse the information between a pair of point clouds, which is helpful to improve the accuracy. (3) INENet can be easily integrated into other overlapping region sensitive algorithms and without adjusting parameters. We conduct experiments on the ModelNet40, S3DIS and 3DMatch data sets. Specifically, the rotation error of the registration algorithm integrated with INENet is improved by at least 25% compared with direct partial overlap registration, our method improves the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1} $ </tex-math></inline-formula> score by 5% and has better anti-noise ability compared with the existing overlap detection methods, showing the effectiveness of the proposed method.

INENet: Inliers Estimation Network With Similarity Learning for Partial Overlapping Registration

An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability: However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open question that how to achieve a satisfying balance between this three key elements. In this paper, we propose BUFFER, a point cloud registration method for balancing accuracy, efficiency, and generalizability. The key to our approach is to take advantage of both point-wise and patch-wise techniques, while overcoming the inherent drawbacks simultaneously. Different from a simple combination of existing methods, each component of our network has been carefully crafted to tackle specific issues. Specifically, a Point-wise Learner is first introduced to enhance computational efficiency by predicting keypoints and improving the representation capacity of features by estimating point orientations, a Patch-wise Embedder which leverages a lightweight local feature learner is then deployed to extract efficient and general patch features. Additionally, an Inliers Generator which combines simple neural layers and general features is presented to search inlier correspondences. Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization. In particular, our method not only reaches the highest success rate on unseen domains, but also is almost 30 times faster than the strong baselines specializing in generalization. Code is available at https://github.com/aosheng1996/BUFFER. 

BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration

Point cloud registration is a research field where the spatial relationship between two or more sets of points in space is determined. Point clouds are found in multiple applications, such as laser scanning, 3D reconstruction, and time-of-flight imaging, to mention a few. This paper provides a thorough overview of recent advances in learning-based 3D point cloud registration methods with an emphasis on non-rigid transformations. In this respect, the available studies should take various challenges like noise, outliers, different deformation levels, and data incompleteness into account. Therefore, a comparison study on the quantitative assessment metrics and robustness of different approaches is discussed. Furthermore, a comparative study on available datasets is reviewed. This information will help to understand the new range of possibilities and to inspire future research directions. 

A review of non-rigid transformations and learning-based 3D point cloud registration methods

/pdf/distinctiveness-oriented-positional-equilibrium-for-point-2oonfrlgvm.pdf

Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration

Point cloud registration is a well-known way to align two different point clouds via a rigid transform estimation in robotics and computer vision applications. In particular, deep learning-based methods have recently attempted to extract highly distinguishable point features for each point. However, it is still challenging to learn highly distinctive point features in different locations but in a similar shape, bringing performance limitations. To address this issue, we propose Geometry Guided Network, namely G $^2$ Net, for point cloud registration with spherical positional encoding method and unsupervised geometry consistency loss. Combined with self-attention, the positional encoding learns the globally unique features by assigning global geometric positional information into irregular 3D points. The uniqueness of each feature is further strengthened with geometric consistency loss across two different point cloud sets. We demonstrate that G $^2$ Net outperforms current state-of-the-arts models in point cloud registration tasks with both full and partial registration experiments for the ModelNet40 and Augmented ICL-NUIM datasets. Various visualizations on learned features are provided for demonstrating the global shape-awareness of our methodology.

Geometry Guided Network for Point Cloud Registration

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Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration

Geometry Guided Network for Point Cloud Registration