arXiv:2606. 01895v1 Announce Type: cross Abstract: With the growing number of satellites in low Earth orbit (LEO) constellations, the near-Earth space environment has become increasingly congested, making space object detection (SOD) a pressing challenge for space safety and sustainability.
arXiv:2607.16862v2 Announce Type: replace Abstract: LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs wit...
The paper presents a dual‑encoder Transformer model for estimating Planetary Boundary Layer Height (PBLH) from satellite radiances, addressing challenges of multimodal, spatially incomplete data. It benchmarks eight different approaches, analyzes model reliance via grouped Shapley decomposition, and demonstrates that the proposed architecture achieves a mean absolute error of 155.8 m on a global test set, outperforming all baselines. On out‑of‑distribution data from the TEAMx campaign, the model attains 165.3 m MAE, better than a pixel‑wise baseline trained on the same data.
arXiv:2605. 14925v2 Announce Type: replace-cross Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.
arXiv:2608.29609v1 Announce Type: new Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...
The paper introduces SolCloudLLM, a large language model–based framework that fuses sky‑image patches with time‑series data through bidirectional multimodal fusion for short‑term solar forecasting. Experiments on the SIRTA and SKIPP'D datasets show that SolCloudLLM outperforms existing baselines, achieving up to a 25.4% reduction in mean squared error, especially under cloudy conditions and in few‑shot scenarios.
DXPR is a depth‑based cross‑modal place recognition framework that matches monocular camera queries to a LiDAR map using a single vision foundation model backbone. By converting both modalities into a unified depth image representation, DXPR learns modality‑invariant global descriptors without modality‑specific encoders. A geometry‑aware overlap miner refines pairwise metric learning by computing pixel‑level overlap scores, and extensive tests on KITTI and Boreas show strong performance across seasons, weather, and day/night conditions, outperforming prior CMPR baselines.
arXiv:2609.39737v1 Announce Type: new Abstract: The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, i...
Cross-modal place recognition (CMPR) aims to identify the same location across heterogeneous sensing modalities, such as vision and LiDAR. Existing methods commonly bridge the modality gap using complex alignment modules, multi-stage training, or full fine-tuning of pretrained backbones.
arXiv:2609.09012v2 Announce Type: replace Abstract: Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, where...
arXiv:2609.00713v1 Announce Type: new Abstract: Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior...
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%.