2026· IEEE Transactions on Computational Imaging· Vol 12, pp. 1333-1346· 0 citations· 28 references
Abstract
With the rapid growth of aerospace activities, space situational awareness (SSA) has become increasingly important for space security. Compared with conventional two-dimensional (2D) inverse synthetic aperture radar (ISAR) image sequences, three-dimensional (3D) representations provide richer structural information. In addition, novel view synthesis (NVS) supports continuous visual interpretation and helps compensate for observation gaps. However, the limited angular coverage of single-pass observations makes stable 3D reconstruction and high-quality NVS difficult without accurate geometric calibration. To address these challenges, Radar Neural Splatting (RNSplat) is proposed for 3D reconstruction and NVS from ISAR image sequences. Specifically, a Pose Head Adaptation via Reprojection (PHARE) module is introduced to refine viewpoint parameters under a cross-view reprojection consistency constraint, thereby improving the estimation of a 3D point map. Together with the associated geometric attributes, the estimated point map is then used to construct the 3D Gaussian splatting (3DGS) representation. To better reflect ISAR image formation characteristics, a Rendering Stabilization Unit (RSU) is further introduced, using a Gaussian-sinc kernel as a PSF-based scattering response approximation to improve cross-view synthesis quality. Experimental results demonstrate that the proposed framework improves NVS performance and enhances visible structural consistency. Ablation studies further show that PHARE improves geometric consistency and 3D point map quality, while RSU enhances the consistency and quality of synthesized views.
Results on LLFF, Mip-NeRF360, and DTU demonstrate that HCSplat effectively regularizes the scene geometry, reconstructs the compact representations, and achieves state-of-the-art novel view synthesis quality under sparse training views.
Hong-an Li, Jiale Yang· Journal of Supercomputing· 0 citations
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Boyang Li, Tianhan Gao, Zuan Gu et al.· Visual Computing for Industr...· 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
Abstract. 3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis (NVS), yet its performance degrades drastically under sparse-view conditions. While existing methods have sought to address this by incorporating accurate 3D geometry via Multi-View Stereo (MVS) or LiDAR priors, the view-dependent appearance parameters (i.e., spherical harmonics) remain exclusively optimized on the limited training views, leading to severe appearance overfitting. This is the fundamental reason why these geometry-enhanced methods still fail to generalize to out-of-distribution (OOD) viewpoints with large baselines, such as lane-changing trajectories in autonomous driving. To address this limitation, we propose VISTA-GS (Virtual Image Synthesis and Training Augmentation), a framework that synergizes MVS-based dense initialization with a physically-grounded virtual view augmentation strategy. Specifically, we position virtual cameras at strategic offsets around the original viewpoints and render virtual training images with binary validity masks via alpha-blending. By computing photometric losses exclusively within valid mask regions, VISTA-GS injects explicit angular constraints into the optimization process, effectively regularizing view-dependent appearance without relying on any external generative model. Experiments on the LLFF benchmark and a real-world LiDAR-scanned dataset demonstrate that our method achieves state-of-the-art NVS quality under sparse-view settings, with particularly significant improvements on challenging OOD viewpoints.
Hongsheng Huang, Yaxin Li, Shengjun Tang et al.· The International Archives o...· 0 citations
This work investigates the integration of geometric priors, in the form of predicted normal and depth maps, into the 3DGS framework to improve the reconstruction quality and reveals that multi-view predictions, as they are done by the recent visual geometry grounded transformer (VGGT), outperform single-view alternatives.
A 3D-GS-based framework that attaches a simplified Disney Bidirectional Reflectance Distribution Function (BRDF) and differentiable physically based rendering (PBR) shader to each surface Gaussian for interpretable material attributes delivers higher geometric accuracy, improved material realism, and real-time rendering performance suitable for applications such as Simultaneous Localization and Mapping (SLAM) and relighting.
Weichen Xu, W. Wan, M. Peng· International Conference on...· 0 citations
