arXiv:2602.14929v2 Announce Type: replace Abstract: Aligning ground-level imagery with geo-registered satellite maps is crucial for mapping, navigation, and situational awareness, yet remains challen...
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv:2608.28891v1 Announce Type: new Abstract: Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be ma...
VI3 is a model‑agnostic framework that grounds pretrained 3D foundation models (3DFMs) by using inertial measurement unit (IMU) data to provide metric scale. It initializes and preintegrates IMU readings to create a metric motion reference, which is then used to recover the scale of 3DFM outputs. The approach includes adaptable anchoring strategies for different 3DFM architectures and demonstrates scale recovery on synthetic and real aerial datasets without ground‑truth supervision.
arXiv:2608.29211v1 Announce Type: new Abstract: Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from uncons...
arXiv:2609.23049v1 Announce Type: new Abstract: Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In rece...
GeoFF3D is a new feed‑forward 3D reconstruction method designed for large‑scale UAV mapping. It uses a coordinate‑anchored model that predicts camera poses and dense point maps directly in a gravity‑aligned Z‑up metric frame, while a spatial large‑scale reconstruction framework (SLRF) partitions images into overlapping chunks, propagates shared‑view priors, and aggregates local reconstructions hierarchically. Across nine aerial mapping blocks, GeoFF3D achieves the best average reconstruction quality, improving F@5 from 0.829 to 0.877, and can reconstruct 2,000 images in about five minutes.
The paper addresses the lack of standardized evaluation for single‑view novel view synthesis by highlighting the importance of aligning a method’s reference frame with the ground‑truth camera pose. It proposes using traditional 2D‑to‑2D and 2D‑to‑3D image matching to align 3D reconstructions and restricts evaluation to co‑visible regions where a correct ground truth exists. Experiments show that with proper alignment, 3D Gaussian‑based methods reach state‑of‑the‑art performance, and a simple baseline using a video generator and geometry estimator matches specialized novel‑view synthesis models without task‑specific training.
GeoGSplat introduces a geometry‑grounded, feed‑forward Gaussian splatting framework that avoids the pixel‑aligned prediction bottleneck by using a fixed number of Gaussians. A dual‑branch encoder builds coarse 3D anchors and fine triplane features, while a hierarchical decoder progressively generates dense 3D points and Gaussian primitives for reconstruction and novel view synthesis. The method outperforms state‑of‑the‑art pixel‑aligned and non‑pixel‑aligned splatting techniques on DTU and NRGBD datasets, especially when rendering views far from the input cameras.
arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...
STARS-GS is a new structure‑aware 3D Gaussian Splatting framework designed for large‑scale aerial surface reconstruction. It introduces a scene partitioning strategy that preserves continuous scene elements, a neighborhood‑aware Gaussian organization that extends geometric constraints to local neighborhoods, and an adaptive surface regularization that tailors regularization strength to local geometry. Experiments on aerial photogrammetry benchmarks show that STARS‑GS improves the average F1‑score from 0.640 to 0.698, a relative gain of about 9.1%.
CrossFit-GS is a new framework for pose-free 3D Gaussian Splatting that avoids the need for Structure-from-Motion by directly reconstructing scenes from unposed image sequences. The method partitions the input into interleaved subsets, maintaining two complementary submaps and cross-refining candidate poses against a frozen opposite submap to provide an independent geometric reference. Additional checkerboard validation and spatial coverage safeguards reject unreliable updates, and only verified poses are committed; after refinement, the final global representation is trained from scratch using the fixed refined poses, leading to reduced trajectory drift and improved camera pose estimation and novel-view synthesis across multiple benchmarks.