We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately $110,000$ remote sensing submaps and $13,000$ LiDAR point cloud submaps captured in urban scenes, and propose a novel method, L2RSI, for cross-view LiDAR place recognition using high-resolution Remote Sensing Imagery. This approach enables large-scale localization capabilities at a reduced cost by leveraging readily available overhead images as map proxies. L2RSI addresses the dual challenges of cross-view and cross-modal place recognition by learning feature alignment between point cloud submaps and remote sensing submaps in the semantic domain. Additionally, we introduce a novel probability propagation method based on particle estimation to refine position predictions, effectively leveraging temporal and spatial information. This approach enables large-scale retrieval and cross-scene generalization without fine-tuning. Extensive experiments on LiRSI-XA demonstrate that, within a $100km^2$ retrieval range, L2RSI accurately localizes $83.27\%$ of point cloud submaps within a $30m$ radius for top-$1$ retrieved location. Our project page is publicly available at https://shizw695.github.io/L2RSI/.

In this paper, we propose a novel neural network called Topology Learning Network (TL-Net), that exploits local and global geometric relation by topology graphs to handle the problem of correspondence filtering in complex scenes. Specifically, we first design a Multi-level Topology Encoder (MLTE), which fuses local and global topology graphs by a channel attention, to sufficiently extract the geometric relation among correspondences. MLTE not only includes local topology graphs by gathering the information of relative motion and multi-resolution group convolution, but also includes a global topology graph by aggregating the information of the similarity and the Graph Laplacian. In addition, inspired by Transformer, we design the backbone of TL-Net to generate enriched fdeature maps for correspondence filtering. Meanwhile, by simplifying the global context aggregation, we maintain the lightweight of the backbone, introducing the superiority of Transformer while avoiding extra parameters and calculations. Empirical experiments on several computer vision tasks show that the performance and generalization ability of TL-Net are significantly superior to the state of the art methods. Notably, on relative pose estimation, we achieve 5.63% and 5.03% mAP improvements under an error threshold of$5^{\circ }$outdoors and indoors, respectively.

Feature matching is an essential computer vision task that requires the establishment of high-quality correspondences between two images. Constructing sparse dynamic graphs and extracting contextual information by searching for neighbors in feature space is a prevalent strategy in numerous previous works. Nonetheless, these works often neglect the potential connections between dynamic graphs from different layers, leading to underutilization of available information. To tackle this issue, we introduce a Sparse Dynamic Graph Interaction block for feature matching. This innovation facilitates the implicit establishment of dependencies by enabling interaction and aggregation among dynamic graphs across various layers. In addition, we design a novel Multiple Sparse Transformer to enhance the capture of the global context from the sparse graph. This block selectively mines significant global contextual information along spatial and channel dimensions, respectively. Ultimately, we present the Multi-layer Sparse Graph Attention Network (MSGA-Net), a framework designed to predict probabilities of correspondences as inliers and to recover camera poses. Experimental results demonstrate that our proposed MSGA-Net surpasses state-of-the-art methods on challenging indoor and outdoor datasets. Code will be available at https://github.com/gongzhepeng/MSGA-Net.

Establishing reliable correspondences between two images is a fundamental and important task in computer vision. This paper proposes a novel network called Sparse Graph Attention Network (SGA-Net), to capture rich contextual information of sparse graphs for feature matching task. Specifically, a graph attention block is proposed to enhance the representational ability of graph-structured features. The proposed block introduces a novel normalization technique for graph-structured features to embed global information into each edge feature, and it adopts the squeeze-and-excitation mechanism to capture graph-wise contextual information. Meanwhile, to further obtain interesting structural information of sparse graphs, a novel sparse graph transformer is developed based on multi-headed self-attention mechanism, while maintaining permutation-equivariance. Additionally, considering that the graph contexts in shallow layers are not fully exploited, a simple graph-context fusion block is introduced to adaptively capture topological information from different layers by implicitly modeling the interdependence between these graph contexts. The proposed SGA-Net can search dependable candidates among the putative correspondences and simultaneously estimate accurate camera poses for two-view geometry estimation. Extensive experiments on outlier removal and camera pose estimation tasks have demonstrated that the proposed SGA-Net outperforms state-of-the-art methods on both outdoor and indoor benchmarks (i.e., YFCC100M and SUN3D). SGA-Net achieves a mAP5° of 58.88% without RANSAC on the outdoor dataset, and it achieves a precision increase of 13.45% and 7.34% compared with the state-of-the-art result on outdoor and indoor datasets, respectively.

Finding reliable correspondences between two images is a fundamental problem in remote-sensing image registration. In the face of sparse and unordered correspondences, the previous learning-based works often focus on global information and ignore valuable local information. To gather rich local information, we propose a simple and effective approach, the principle of which is to establish a local neighborhood structure for putative correspondences and then extract and aggregate neighborhood context. Specifically, we design a residual block with an innovative normalization operation and then we construct a sub-net, by introducing a competition mechanism in the local neighborhood to enhance the expression ability of features. Finally, we propose a learning-based network, to improve the performance of outlier rejection by extracting neighborhood context and global context. Extensive experiments on relative pose estimation demonstrate that the proposed network surpasses current state-of-the-art approaches on both challenging indoor and outdoor datasets.

In this paper, we propose a novel multi-scale attention based network (called MSA-Net) for feature matching problems. Current deep networks based feature matching methods suffer from limited effectiveness and robustness when applied to different scenarios, due to random distributions of outliers and insufficient information learning. To address this issue, we propose a multi-scale attention block to enhance the robustness to outliers, for improving the representational ability of the feature map. In addition, we also design a novel context channel refine block and a context spatial refine block to mine the information context with less parameters along channel and spatial dimensions, respectively. The proposed MSA-Net is able to effectively infer the probability of correspondences being inliers with less parameters. Extensive experiments on outlier removal and relative pose estimation have shown the performance improvements of our network over current state-of-the-art methods with less parameters on both outdoor and indoor datasets. Notably, our proposed network achieves an 11.7% improvement at error threshold 5° without RANSAC than the state-of-the-art method on relative pose estimation task when trained on YFCC100M dataset.