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,
08/2024, Volume:
46, Issue:
8
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.
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.
Recently, 3D point cloud classification has made significant progress with the help of many datasets. However, these datasets do not reflect the incomplete nature of real-world point clouds caused by ...occlusion, which limits the practical application of current methods. To bridge this gap, we propose ModelNet-O, a large-scale synthetic dataset of 123,041 samples that emulates real-world point clouds with self-occlusion caused by scanning from monocular cameras. ModelNet-O is 10 times larger than existing datasets and offers more challenging cases to evaluate the robustness of existing methods. Our observation on ModelNet-O reveals that well-designed sparse structures can preserve structural information of point clouds under occlusion, motivating us to propose a robust point cloud processing method that leverages a critical point sampling (CPS) strategy in a multi-level manner. We term our method PointMLS. Through extensive experiments, we demonstrate that our PointMLS achieves state-of-the-art results on ModelNet-O and competitive results on regular datasets such as ModelNet40 and ScanObjectNN, and we also demonstrate its robustness and effectiveness. Code available: https://github.com/fanglaosi/ModelNet-O_PointMLS.
Display omitted
•We introduce a challenging occlusion point cloud classification dataset ModelNet-O.•We propose a robust method, PointMLS, based on a multi-level sampling strategy.•We achieve SoTA results on the ModelNet-O and competitive results on regular datasets.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
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.
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are ...sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance.
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While ...hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed
EdgeConv
suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.
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
