Geospatial-Prior Guidance for 3D Semantic Scene Completion

SAM3R: Object-Centric 3D Mapping via Foundation-Model-Guided Data Association in Changing Scenes

For embodied agents to navigate and reason indoor spaces, they need object-level 3D representations that stay consistent over time as new frames arrive from a monocular camera. Current online 3D instance segmentation methods either depend on posed RGB-D input with ground-truth depth or couple tightly to the internal representations of specific foundation models, sacrificing modularity. We observe that appearance-based and geometry-based object matching exhibit complementary failure modes: appearance is ambiguous among spatially separated duplicates, while geometry is unreliable for visually distinct objects at similar locations. This motivates SAM3R, a training-free pipeline that fuses spatial overlap, 3D centroid displacement, and visual-semantic similarity into a single assignment cost solved via bipartite matching. The cost is constructed entirely from the outputs of frozen foundation models without accessing internal representations. Object tracks are classified through a cascaded decision tree that detects scene changes via field-of-view gated temporal voting. On ScanNet200 and Replica, SAM3R performs competitively with methods that require architecture-specific features or additional training, despite operating in a fully online, monocular setting. Qualitative evaluation on the Aria Digital Twin dataset further demonstrates that the pipeline maintains correct object identities through physical object manipulation, including hand occlusion and large spatial displacement.

GeoFeatureGS: block-consistent semantic feature learning for large-scale geographic scenes with 3D gaussian splatting

Urban-Graph: Bridging Local SLAM and Global Earth Observation for Fine-Grained Urban LCLU Mapping

Abstract. Urban scene understanding requires both global geographic context and local structural detail. Earth Observation (EO) imagery supports large-scale land-cover and land-use (LCLU) mapping, but in urban areas it often merges heterogeneous surfaces into broad built-up classes. Vehicle-based sensors such as LiDAR and cameras recover these local structures, but their maps can drift and often remain in a local coordinate frame. We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph. Coarse georeferenced regions from EO data are updated with local observations, while a factor graph jointly optimises SLAM constraints and global geodetic constraints. The resulting graph is projected back to the overhead layer to separate coarse urban classes into finer semantic components. Experiments in CARLA show improved global alignment, reduced drift, and more detailed projection of local semantics into EO space.

RayLift: Lifting Complementary Ray-Wise Evidence with 3D Geometry Priors for Semantic Scene Completion

Camera-based 3D semantic scene completion (SSC) provides comprehensive scene understanding for autonomous driving and robotics. However, existing methods often treat stereo depth estimates as deterministic geometric constraints, causing depth uncertainty and local correspondence errors to propagate directly into voxel representations. To address this issue, we propose RayLift, a framework that uses stereo geometry as a metric reference while incorporating complementary ray evidence to recover reliable 3D structures adaptively. RayLift first employs a Complementary Context Encoder that extracts geometry-aware priors from a frozen 3D vision foundation model, thereby enriching the scene context. It then introduces a Depth Ray Evidence Lifter module that jointly models geometric dissimilarity, depth confidence, and spatial uncertainty to adaptively sample and weight candidate surface locations along each camera ray. Finally, a Semantic-Aware Voxel Integrator injects the resulting ray evidence into voxel features by explicitly modeling their spatial support. Extensive experiments on SemanticKITTI and SSCBench-KITTI-360 demonstrate that RayLift achieves competitive performance and consistently outperforms existing methods.

STARS-GS: Structure-Aware Regularized Gaussian Splatting for Large-Scale Aerial Surface Reconstruction

Large-scale 3D surface reconstruction from aerial imagery is fundamental to geospatial mapping and urban modeling. Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated considerable potential for this task. However, existing methods still face three major challenges in large and complex scenes: scene partitioning may split continuous scene elements across independently optimized sub-regions; geometric constraints mainly focus on the attributes of individual Gaussians while overlooking their local organization; and uniform regularization struggles to accommodate heterogeneous geometric structures. To address these issues, we propose STARS-GS, a structure-aware 3DGS framework for large-scale surface reconstruction. First, we introduce a structure-aware scene partitioning strategy that better preserves continuous scene structures during partitioning and reduces cross-region geometric inconsistencies and stitching artifacts through boundary refinement. Second, we develop neighborhood-aware Gaussian organization that extends geometric constraints from individual primitives to their neighborhood organization, encouraging Gaussians to better conform to local surface geometry. Third, we introduce adaptive surface regularization that adjusts the regularization strength according to local geometric characteristics, promoting geometric consistency in structured regions while preserving plausible variations in unstructured regions. Extensive experiments on large-scale aerial photogrammetry benchmarks demonstrate that STARS-GS consistently outperforms the evaluated Gaussian-based methods in surface reconstruction. It increases the average F1-score from 0.640 for the second-best method to 0.698, corresponding to a relative improvement of approximately 9.1\%, demonstrating effective improvements in geometric accuracy and surface completeness.

Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction

Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from unconstrained image collections. Existing cross-view localization methods have strict requirements such as panoramic imagery or known initial locations, limiting their applicability for in-the-wild reconstruction settings. We propose a robust hierarchical cross-view localization framework that leverages geometric constraints from Structure-from-Motion (SfM) models derived from unconstrained ground image collections. Our method generates coarse-to-fine pose hypotheses through a cross-view matching approach and aggregates noisy predictions across SfM model(s) using Kernel Density Estimation to recover consensus alignments while filtering outliers. Experiments demonstrate reliable localization performance from challenging image collections. Empirically we found satellite-referenced alignment enables accurate metric scale estimation, doppelg\"anger detection, and merging of disjoint SfM reconstructions, resulting in more complete, geo-localized site models than are possible with SfM alone.