Mars exploration is an advanced field of global deep space exploration. Accurate three-dimensional reconstruction of the Martian surface topography is very important for autonomous navigation, scientific target recognition, and operation planning. In order to meet the analysis requirements of the Martian surface scene, this paper proposes an explicit surface-geometry-constrained Gaussian splatting (ESGS) method. Firstly, this method includes a normal and depth prior estimation network (NDN) that generates normal and depth priors from Martian surface image data, thereby promoting the fusion of semantic and multi-view contextual information to enhance the geometric accuracy of 3D reconstruction of the Martian surface. Secondly, we designed the Gaussian parameter-based deformable fusion network (GPDFN) to fuse multi-receptive-field feature information. Finally, we collected Martian surface remote sensing images from NASA, constructed a Martian surface 3D reconstruction dataset named Mars_3D using the COLMAP method, annotated depth and normal labels for its seven real-world scenes and two Blender-generated scenes, and conducted comparative experiments with eight excellent algorithms on this dataset to validate the effectiveness of our method in 3D reconstruction of the Martian surface using remote sensing images. Experiments show that the average SSIM of the ESGS method in this article is 0.6946, PSNR is 23.40 dB, and LPIPS is 0.253 on the Mars_3D dataset, demonstrating superior overall performance compared to all other models and enhancing the quality of 3D reconstruction of the Martian surface.
Abstract. High-precision digital terrain models (DTMs) are important for Mars explorations and research, providing indispensable spatial information for landing site assessment, rover path planning, and surface environment analysis. However, challenges such as high-resolution stereo data scarcity and complex atmospheric conditions on the Martian surface result in traditional terrain reconstruction methods suffer from limitations in accuracy, coverage and resolution. To enhance the model’s ability to recover fine-grained topography, we present a diffusion-based monocular terrain reconstruction method, which progressively recovers Martian terrains from single-view high-resolution optical images. We employed a multi-scale U-Net denoising network with attention mechanisms and introduced an additional end-to-end depth constraint. To improve terrain reconstruction efficiency, we implemented a diffusion model in the latent space and adopted a skipping sampling mechanism. We employed the proposed method to reconstruct terrain in different regions. Experimental results demonstrate that the reconstructed terrain achieves an accuracy of 2 m. Furthermore, compared to photogrammetric terrain, the shaded relief generated by our method exhibits greater similarity to the input imagery.
Jiarui Cao, Rong Huang, Yusheng Xu et al.· The International Archives o...· 0 citations
Abstract. Mars topographic data are crucial for quantitative surface characterization, exploration missions, and studies of Martian surface processes. Photogrammetric processing of Mars orbital imagery is a majormethod for generating three-dimensional (3D) terrain models, such as digital elevation models (DEMs), with bundle adjustment (BA) serving as the key step for mitigating inconsistencies in overlapping regions of different orbital images and further improving the spatial accuracy of the resulting DEMs. However, BA performance is often limited by the texture-less Martian surface and the lack of ground control points. To address this issue, this paper proposes a BA method assisted by robust crater graph features. Since impact craters are widely distributed on Mars, they can serve as valuable semantic priors for accurate topographic mapping. The method first uses deep learning to extract crater structures and constructs crater graphs based on the minimum spanning tree rule. It then searches for corresponding crater graphs across different images to identify crater correspondences and robust tie points. Finally, angular relationships among adjacent craters are introduced into BA observation equations to enhance adjustment performance and reduce geometric inconsistencies. serve as valuable semantic priors for accurate topographic mapping. Experiments conducted over the McLaughlin Crater area using CTX stereo images, with HRSC-derived DTMs and orthoimages as reference data, demonstrate that the proposed method effectively improves the precision and stability of BA and supports high-accuracy 3D mapping of the Martian surface.
Haonan Zhong, Zhaojin Li, Bo Wu· The International Archives o...· 0 citations
Abstract. Satellite imagery offers a distinct advantage in Earth observation by providing expansive coverage and enabling the monitoring of inaccessible regions without physical on-site intervention, serving as a significantly more cost-effective and scalable alternative to traditional aerial or ground-based surveys. The task of 3D reconstruction from multi-view satellite images has therefore been a pivotal point of research at the intersection of photogrammetry and remote sensing. Recently, novel-view synthesis techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have accelerated the accuracy and speed of topographic modeling. Among these, Earth Observation Gaussian Splatting (EOGS) has emerged as a state-of-the-art approach by adapting 3DGS to handle the unique geometric and radiometric characteristics of satellite data, including Rational Polynomial Coefficients (RPCs) and varying solar conditions. However, the standard EOGS pipeline relies on stochastic initialization, where Gaussians are distributed uniformly within a volumetric bounding box, leading to high computational overhead and dependency on aggressive pruning that can inadvertently remove critical geometric features, particularly in areas with complex urban structures. To address these limitations, we propose Bundle-Adjusted Initialization for Earth Observation Gaussian Splatting, which leverages sparse point clouds from bundle adjustment as geometric priors for Gaussian initialization. Combined with an adaptive densification strategy, our method achieves faster convergence and improved DSM accuracy on the DFC2019 dataset compared to the EOGS baseline.
Jiyong Kim, Shuang Song, Rongjun Qin· The International Archives o...· 0 citations
Multi-modal 3D object detection is an important task in autonomous driving systems, where cameras and LiDAR provide complementary semantic and geometric information. Most existing BEV fusion methods are designed based on the Cartesian representation space, which does not fully match the sensing geometry of camera and LiDAR. This generally leads to redundant computation in distant regions. To address this issue, GARF, a geometry-aware polar BEV framework, is presented for multi-modal 3D object detection. GARF organizes camera and LiDAR features in a unified polar BEV space, which can represent spatial resolution more compactly. For the camera branch, the uncertainty-guided transformation of the polar view is designed to improve the reliability of depth estimation. Then, the generated polar BEV feature is further refined to attenuate radial noise and angular discontinuity. For the LiDAR branch, the polar-aware sparse feature extraction and distortion correction modules are designed to deal with the anisotropic structure and geometric distortion caused by polar voxelization. For multi-modal fusion, the region-aware cross-modal fusion strategy and polar detection head with anisotropic Gaussian center response map are developed, which achieve effective feature interaction and consistent geometry supervision. The experimental results on nuScenes show that GARF achieves 71.8% mAP and 73.7% NDS, improving the baseline by 3.3% mAP and 2.3% NDS. Meanwhile, the inference speed increases from 7.1 FPS to 8.9 FPS, and the consumption of GPU memory decreases from 41,114 MiB to 33,346 MiB.
Feng Gao, Jiaxin Chen, Niuniu Wang· Italian National Conference...· 0 citations
Abstract. High-resolution 3D documentation of cultural heritage sites is essential for their preservation. While terrestrial laser scanning (TLS) remains the gold standard, it is often cost-intensive compared to photogrammetry. This study evaluates three image-based reconstruction techniques, Multi-View Stereo (MVS), Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), by applying them to a complex scene featuring a chapel and its surrounding vegetation, sensed from an uncrewed aerial vehicle (UAV). A hybrid TLS/MVS model provides a high-accuracy reference. Using identical interior and exterior camera parameters of the 105 UAV-acquired images, we generate dense point clouds with all methods and assess geometric accuracy and completeness using the M3C2 algorithm. Results show that MVS achieves superior accuracy (standard deviation of all M3C2 distances: MVS = 0.11 m, NeRF = 0.15 m), whereas NeRF attains up to 20% higher completeness, particularly in low-texture and vegetation-occluded regions. The 3DGS point cloud was deemed too sparse and was therefore not used for further analysis. The study highlights the potential of NeRFs to recover partially occluded or sparsely textured geometries that are challenging for MVS and suggests a complementary use of both approaches for cost-efficient documentation of cultural heritage.
Frederik Schulte, P. Akwensi, L. Winiwarter· The International Archives o...· 0 citations
RG-PSR is presented, a reliability-guided enhancement framework for Poisson-family surface reconstruction from degraded 3D-imaging point clouds and is positioned as a practical reliability layer for coherent Poisson-family reconstruction rather than a universal replacement for all surface-reconstruction methods.
Na Liu, Fan Zhang, Jia-Wei Wang et al.· Journal of Imaging· 0 citations
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