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 ( 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 SACF-Net yields unprecedented performance improvements.
Sequential Point Clouds: A Survey Wang, Haiyan; Tian, Yingli
IEEE transactions on pattern analysis and machine intelligence,
2024-Feb-14, Volume:
PP
Journal Article
Peer reviewed
Open access
Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on ...sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.
Partial point cloud registration aims to transform partial scans into a common coordinate system. It is an important preprocessing step to generate complete 3D shapes. Although previous registration ...methods have made great progress in recent decades, traditional registration methods, such as Iterative Closest Point (ICP) and its variants, all these methods highly depend on the sufficient overlaps between two point clouds, because they cannot distinguish outlier correspondences. Note that the overlap between point clouds could always be small, which limits the application of these methods. To tackle this problem, we present a StrucTure-based OveRlap Matching (STORM) method for partial point cloud registration. In our method, an overlap prediction module with differentiable sampling is designed to detect points in overlap utilizing structure information, and facilitates exact partial correspondence generation, which is based on discriminative pointwise feature similarity. The pointwise features which contain effective structural information are extracted by graph-based methods. Experimental results and comparison with state-of-the-art methods demonstrate that STORM can achieve better performance. Moreover, most registration methods perform worse when the overlap ratio decreases, while STORM can still achieve satisfactory performance when the overlap ratio is small.
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc .) often project the point clouds to 2D ...space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. The proposed model acts as a backbone and the learned features from this model can be used for downstream tasks such as point cloud semantic and panoptic segmentation or 3D detection. In this paper, we benchmark our model on these three tasks. For semantic segmentation, we evaluate the proposed model on several large-scale datasets, i.e., SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge), and significantly outperforms existing methods on nuScenes and A2D2 dataset. Furthermore, the proposed 3D framework also shows strong performance and good generalization on LiDAR panoptic segmentation and LiDAR 3D detection.
Indoor perception is a field that has gained traction in recent years. While there has been a significant amount of research done on outdoor perception and motion planning, the indoor environment has ...yet to receive similar treatment. In an indoor environment, various sensor systems have been developed to track and localize objects, each tackling a different set of challenges. In this paper, we introduce a novel Infrastructure Sensor Node (ISN) consisting of a LiDAR along with two monocular cameras mounted on the ceiling of the hallways of our lab to obtain relevant information. We present a perception pipeline that uses prior 3D point cloud registration to localize objects in real-time in cluttered indoor environments. We provided a complete case study to present a work that successfully detects, registers, and localizes objects through a cluttered environment with a high degree of occlusion.
Cross-source (CS) point cloud registration is a prerequisite for effectively leveraging the complementary information of multiple 3-D sensors. However, existing point cloud registration methods have ...primarily focused on the registration of mono-source point clouds and typically fail to register CS data with varying noise patterns and capture characteristics. In this article, we present a new algorithm for CS point cloud registration between mobile laser scanning (MLS) point clouds and stereo-reconstructed point clouds (SPCs). Our method has two key designs. First, we design a novel descriptor with in-plane rotation equivariance by leveraging the accessible gravity prior, yielding strong descriptiveness, better robustness, and improved efficiency. Second, based on the noise pattern of SPCs, a novel disparity-weighted correspondence scoring strategy is proposed to strengthen the registration accuracy. In comparison to existing registration baselines, our method achieves a 32.6% higher registration recall (RR) on CS datasets of KITTI and KITTI-360 and a 23.1% higher RR on mono-source datasets of KITTI. Notably, our method also outperforms RANdom SAmple Consensus (RANSAC)-based methods in terms of computational efficiency with a 10<inline-formula> <tex-math notation="LaTeX">\times \,\,\sim </tex-math></inline-formula> 70<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> speedup. The source code and datasets have been available at https://github.com/WHU-USI3DV/MSReg .
Mutual Voting for Ranking 3D Correspondences Yang, Jiaqi; Zhang, Xiyu; Fan, Shichao ...
IEEE transactions on pattern analysis and machine intelligence,
2024-June, 2024-Jun, 2024-6-00, 20240601, Volume:
46, Issue:
6
Journal Article
Peer reviewed
Open access
Consistent correspondences between point clouds are vital to 3D vision tasks such as registration and recognition. In this paper, we present a mutual voting method for ranking 3D correspondences. The ...key insight is to achieve reliable scoring results for correspondences by refining both voters and candidates in a mutual voting scheme. First, a graph is constructed for the initial correspondence set with the pairwise compatibility constraint. Second, nodal clustering coefficients are introduced to preliminarily remove a portion of outliers and speed up the following voting process. Third, we model nodes and edges in the graph as candidates and voters, respectively. Mutual voting is then performed in the graph to score correspondences. Finally, the correspondences are ranked based on the voting scores and top-ranked ones are identified as inliers. Feature matching, 3D point cloud registration, and 3D object recognition experiments on various datasets with different nuisances and modalities verify that MV is robust to heavy outliers under different challenging settings, and can significantly boost 3D point cloud registration and 3D object recognition performance.
PDF
