Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficiency, existing methods remain unsuitable for satellite images due to incompatibility with rational polynomial coefficient (RPC) models and limited generalization capability. Recent advances in generalizable 3DGS approaches show potential, but they perform poorly on multi-temporal sparse satellite images due to limited geometric constraints, transient objects, and radiometric inconsistencies. To address these limitations, we propose SkySplat, a novel self-supervised framework that integrates the RPC model into the generalizable 3DGS pipeline, enabling more effective use of sparse geometric cues for improved reconstruction. SkySplat relies only on RGB images and radiometric-robust relative height supervision, thereby eliminating the need for ground-truth height maps. Key components include a Cross-Self Consistency Module (CSCM), which mitigates transient object interference via consistency-based masking, and a multi-view consistency aggregation strategy that refines reconstruction results. Compared to per-scene optimization methods, SkySplat achieves an 86 times speedup over EOGS with higher accuracy. It also outperforms generalizable 3DGS baselines, reducing MAE from 13.18 m to 1.80 m on the DFC19 dataset significantly, and demonstrates strong cross-dataset generalization on the MVS3D benchmark.

3D Gaussian Splatting (3DGS) has attracted considerable attention due to its remarkable rendering efficiency and superior visual quality. However, typical implementations struggle with ultra-high-resolution imagery due to excessive GPU memory demands, a critical limitation particularly in domains such as aerial mapping and remote sensing, where fine-grained details are crucial for accurate 3D reconstruction. In this paper, we introduce InfiniGS, an enhanced Gaussian Splatting framework that scales up training image resolution while maintaining fidelity and efficiency, enabling reconstruction from ultra-high-resolution images without encountering out-of-memory (OOM) issues. Our framework consists of two modules; firstly, we propose a Multi-Resolution Training strategy that guides the optimization from recovering global structures toward fine-grained details, while mitigating erosion artifacts during resolution transitions. Secondly, we introduce an Image Block Partitioning and Asynchronous Optimization scheme, which significantly reduces GPU memory overhead. Most importantly, we reveal the scaling rule for the densification threshold of Gaussian primitives and the hyperparameters of the Adam optimizer during resolution transitions and image partitioning. This ensures consistently high rendering quality regardless of the multi-resolution factor or partitioning strategy employed. Extensive experiments demonstrate that InfiniGS achieves superior Novel View Synthesis (NVS) for high-resolution scenes, while requiring less memory and shorter training time. To the best of our knowledge, InfiniGS is the first method capable of reconstructing scenes with resolutions up to 10 K and beyond, at full resolution and high fidelity, without exceeding the memory capacity of mainstream powerful GPUs (e.g., NVIDIA RTX 3090 with 24 GB VRAM).

Although existing texture synthesis methods perform well in generating large images with irregularly repeated textures to avoid visually unrealistic repetitions, they still face significant challenges in synthesizing regular textures with densely interconnected structures. In this paper, we propose a novel neural texture synthesis method, FreNTS, which uses frequency domain information to enhance the texture synthesis process, synthesizing textures with continuous, complete, and visually realistic overall structures. The core idea is to perform the Discrete Cosine Transform on image patches to obtain the corresponding frequency domain rate information features, and then use the designed adaptive guided correspondence (AGC) loss to calculate the correlation difference between the source image and the target image in the frequency domain and spatial domains, thereby constraining the optimization of the target image to achieve high-quality texture synthesis. In addition, to better evaluate the effect of texture synthesis, we introduce Tile LPIPS as the metric for quantitative evaluation. Experimental results show that the proposed FreNTS can effectively accelerate the process of neural texture synthesis and use high-frequency information to capture better structural details to synthesize realistic textures.

Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map to establish coarse matches, limiting further improvements in accuracy and efficiency. Motivated by this limitation, we propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance. Specifically, the matching stage is decomposed into two progressive phases, bridged by a region-based selective cross-attention mechanism designed to enhance feature discriminability. In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas identified in the first phase. Additionally, this pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction. The acceleration gains of CasP increase with higher resolution, and our lite model achieves a speedup of $\sim2.2\times$ at a resolution of 1152 compared to the most efficient method, ELoFTR. Furthermore, extensive experiments demonstrate its superiority in geometric estimation, particularly with impressive cross-domain generalization. These advantages highlight its potential for latency-sensitive and high-robustness applications, such as SLAM and UAV systems. Code is available at https://github.com/pq-chen/CasP.

Stereo image super-resolution (SSR) aims to enhance high-resolution details by leveraging information from stereo image pairs. However, existing stereo super-resolution (SSR) upsampling methods (e.g., pixel shuffle) often overlook cross-view geometric consistency and are limited to fixed-scale upsampling. The key issue is that previous upsampling methods use convolutions to independently process deep features of different views, lacking cross-view and non-local information perception, making it difficult to select beneficial information from multi-view scenes adaptively. In this work, we propose Stereo Implicit Neural Representation (StereoINR), which innovatively models stereo image pairs as continuous implicit representations. This continuous representation breaks through the scale limitations, providing a unified solution for arbitrary-scale stereo super-resolution reconstruction of left-right views. Furthermore, by incorporating spatial warping and cross-attention mechanisms, StereoINR enables effective cross-view information fusion and achieves significant improvements in pixel-level geometric consistency. Extensive experiments on multiple datasets demonstrate that StereoINR outperforms out-of-training-distribution scale upsampling and matches state-of-the-art SSR methods within training-distribution scales.

Semantic 3D reconstruction from multi-view images is essential for applications such as 3D city modeling and robot navigation. However, existing methods treat semantic segmentation and height estimation as separate tasks, leading to suboptimal reconstruction results. To bridge this gap, we introduce MVSR3D, the first end-to-end framework for semantic 3D reconstruction using multi-view satellite images. MVSR3D employs a dual-stream architecture, consisting of the segmentation branch (MVSAM) based on Segment Anything Model (SAM) and the height estimation branch based on multi-view stereo. To enhance multi-view feature fusion, we propose the Epipolar Cross Attention (ECA) module in the MVSAM branch, which integrates image embeddings primarily along epipolar line to exploit complementary multi-view information. Unlike conventional multi-task learning approaches, we design dedicated interaction modules—the SAM Feature-Guided (SAM-FG) module and the Elevation-Guided Sparse Prompts Generator (EGSPG)—to facilitate multi-task interaction and feature fusion. Extensive evaluations on the DFC19 and SpaceNet4 datasets demonstrate that MVSR3D significantly outperforms the state-of-the-art multi-view multi-task learning method, improving the mIoU3 metric at a 2.5-meter threshold by 37.09%–45.11%.

Compact low-poly building models with concise structures and texture fidelity are essential infrastructure for digital twin cities. Traditional point cloud-based reconstruction methods often rely on surface normals, and the presence of missing data and noise poses significant challenges for accurate reconstruction. In this paper, we propose GS2Poly, a textured polygonal mesh reconstruction method for buildings based on the 3D Gaussian Splatting (3DGS) framework. Firstly, 3DGS of the building scene is reconstructed under planar structure constraints. A density-weighted Gaussian sampling method is utilized to sample high-quality surface point clouds and extract planar primitives from 3DGS reconstruction results. Next, GS2Poly applies an adaptive spatial partitioning strategy to generate a set of candidate convex polyhedra. Finally, guided by the Gaussian opacity field, a Markov random field is constructed to extract the polygonal mesh surface, followed by high-fidelity texture mapping using an optimal rendering strategy. Experimental results across diverse building scenarios demonstrate that GS2Poly exhibits higher geometric fidelity than spatial partitioning-based or 3DGS-based mesh simplification methods. Additionally, the proposed texture mapping strategy effectively avoids typical texture artifacts such as occlusion, seams and distortions.

Extrinsic calibration is an essential prerequisite for the applications of camera-LiDAR fusion. Existing methods either suffer from the complex offline setting of man-made targets or tend to produce suboptimal and unrobust results. In this paper, we propose an online two-stage calibration method that estimates robust and accurate extrinsic parameters between camera and LiDAR. This is a novel work to use semantic information and geometric features jointly in calibration to promote accuracy and robustness. In the first stage, we detect objects in the image and point cloud and build graphs on the objects using Delaunay triangulation. Then, we design a novel graph matching algorithm to associate the objects in the two data domains and extract pairs of 2D-3D points. Using the PnP solver, we get robust initial extrinsic parameters. Then, in the second stage, we design a new optimization formulation with semantic information and geometric features to generate accurate extrinsic parameters with the initial value from the first stage. Extensive experiments on solid-state LiDAR, conventional spinning LiDAR and KITTI datasets have verified the robustness and accuracy of our method which outperforms existing works. We will share the code publicly to benefit the community (after review stages).

Loop Closure Detection (LCD) is an essential component of Simultaneous Localization and Mapping (SLAM), helping to correct drift errors, facilitate map merging, or both by identifying previously observed scenes. Despite its importance, traditional LCD algorithms based on single sensor such as camera or LiDAR exhibit degraded performance in challenging scenarios due to their inherent limitations. To address this issue, we propose a novel LCD method based on camera-LiDAR fusion, exploiting the rich textural information from cameras and the accurate geometric data from LiDAR to ensure robustness and speed in challenging environments. Specifically, we first employ deep hashing learning to encode deep image features into binary image descriptors for extremely fast loop candidate (LC) retrieval. Then, LiDAR points are augmented with image color for accurate geometric verification. Finally, we incorporate a spatial-temporal consistency check that mandates an LC to have consistently matched neighbors to be accepted as true. Our method is extensively verified and compared with the state-of-the-art methods on various datasets encompassing both indoor and outdoor environments. Experimental results demonstrate that our method obtains the best performance, increasing the maximum recall rate at 100% precision by a significant margin of 20% while operating in real-time at an average speed of 30 fps.

Building individual segmentation plays a crucial role in building querying, management, analysis, and attribute addition. Previous research on this topic has primarily concentrated on small-scale scenes and single-type buildings. However, when dealing with complex scenes that contain diverse buildings, existing methods for building individual segmentation often encounter challenges, such as excessive undersegmentation and oversegmentation. To tackle this issue, we propose a scene adaptive building individual segmentation (SABIS) based on large-scale airborne LiDAR point clouds. The method first segments the roof object and then extract elevation feature and area feature of the roof object. Based on these features, the building point cloud is classified into two categories: urban scene buildings and rural residential scene buildings. Finally, for urban scene buildings, the building individual segmentation method based on the cylinder model consistency is used. For rural residential scene buildings, the building individual segmentation method based on bidirectional saliency features is employed. In this article, the proposed SABIS algorithm is quantitatively evaluated by using three large scene datasets at home and abroad and four benchmark methods. All kinds of accuracy are significantly better than the most advanced algorithms.

Maximum correntropy criterion (MCC) is a robust and powerful technique to handle heavy-tailed nonGaussian noise, which has many applications in the fields of vision, signal processing, machine learning, etc. In this article, we introduce several contributions to the MCC and propose an augmented MCC (AMCC), which raises the robustness of classic MCC variants for robust fitting to an unprecedented level. Our first contribution is to present an accurate bandwidth estimation algorithm based on the probability density function (PDF) matching, which solves the instability problem of the Silverman's rule. Our second contribution is to introduce the idea of graduated nonconvexity (GNC) and a worst-rejection strategy into MCC, which compensates for the sensitivity of MCC to high outlier ratios. Our third contribution is to provide a definition of local distribution measure to evaluate the quality of inliers, which makes the MCC no longer limited to random outliers but is generally suitable for both random and clustered outliers. Our fourth contribution is to show the generalizability of the proposed AMCC by providing eight application examples in geometry perception and performing comprehensive evaluations on five of them. Our experiments demonstrate that 1) AMCC is empirically robust to 80%$-$90% of random outliers across applications, which is much better than Cauchy M-estimation, MCC, and GNC-GM; 2) AMCC achieves excellent performance in clustered outliers, whose success rate is 60%$-$70% percentage points higher than the second-ranked method at 80% of outliers; 3) AMCC can run in real-time, which is 10$-$100 times faster than RANSAC-type methods in low-dimensional estimation problems with high outlier ratios. This gap will increase exponentially with the model dimension.

This letter proposes a LiDAR and image line-guided stereo matching method (L2GSM), which combines sparse but high-accuracy LiDAR points and sharp object edges of images to generate accurate and fine-structure point clouds. After extracting depth discontinuity lines on the image by using LiDAR depth information, we propose a trilateral update of cost volume and depth discontinuity lines-aware semi-global matching (SGM) strategies to integrate LiDAR data and depth discontinuity lines into the dense matching algorithm. The experimental results for the indoor and aerial datasets show that our method significantly improves the results of the original SGM and outperforms two state-of-the-art LiDAR constraints’ SGM methods, especially in recovering the 3-D structure of low-textured and depth discontinuity regions. In addition, the 3-D point clouds generated by our proposed method outperform the LiDAR data and dense matching point clouds generated by Metashape and SURE aerial in terms of completeness and edge accuracy.

Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segment reconstruction algorithms deal with two views or dozens of small-size images; while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR11Available at https://skyearth.org/publication/project/ELSR. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images.

Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps: (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horizontal and vertical directions to optimize the matching distance function. The optimization results then are rematched, and the outliers are eliminated using a fast sample consensus algorithm. We performed comparison experiments on our CoFSM method with the scale-invariant feature transform (SIFT), upright-SIFT, PSO-SIFT, and radiation-variation insensitive feature transform (RIFT) methods using a multi-modal image dataset. The algorithms of each method were comprehensively evaluated both qualitatively and quantitatively. Our experimental results show that our proposed CoFSM method can obtain satisfactory results both in the number of corresponding points and the accuracy of its root mean square error. The average number of obtained matches is namely 489.52 of CoFSM, and 412.52 of RIFT. As mentioned earlier, the matching effect of the proposed method was significantly greater than the three state-of-art methods. Our proposed CoFSM method achieved good effectiveness and robustness. Executable programs of CoFSM and MRSI datasets are published: https://skyearth.org/publication/project/CoFSM/.

Water body extraction from remote sensing imagery is an essential and nontrivial issue due to the complexity of the spectral characteristics of various kinds of water bodies and the redundant background information. An automatic multifeature water body extraction (MFWE) method integrating spectral and spatial features is proposed in this letter for water body extraction from GF-1 multispectral imagery in an unsupervised way. This letter first discusses a spatial feature index, called the pixel region index (PRI), to describe the smoothness in a local area surrounding a pixel. PRI is advantageous for assisting the normalized difference water index (NDWI) in detecting major water bodies, especially in urban areas. On the other hand, part of the water pixels near the borders may not be included in major water bodies, k-means clustering is subsequently conducted to cluster all the water pixels into the same group as a guide map. Finally, the major water bodies and the guide map are merged to obtain the final water mask. Our experimental results demonstrate that accurate water masks were achieved for all seven GF-1 imagery scenes examined. Three images with a complex background and water conditions were used to quantitatively compare the proposed method to NDWI thresholding and support vector machine classification, which verified the higher accuracy and effectiveness of the proposed method.

Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance.

In order to solve the accuracy problem caused by lens distortions of nonmetric digital cameras mounted on an unmanned aerial vehicle, the estimation for initial values of lens distortion must be studied. Based on the fact that radial lens distortions are the most significant of lens distortions, a simple and efficient method for radial lens distortion estimation is proposed in this paper. Starting from the coplanar equation, the geometric characteristics of the relative orientation equations are explored. This paper further proves that the radial lens distortion can be linearly estimated in a continuous relative orientation model. The proposed procedure only requires a sufficient number of point correspondences between two or more images obtained by the same camera; thus it is suitable for a natural scene where the lack of straight lines and calibration objects precludes most previous techniques. Both computer simulation and real data have been used to test the proposed method; the experimental results show that the proposed method is easy to use and flexible.