Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or short-range indoor scans, or consist of synthetic data that lacks the semantic complexity of the physical world.
arXiv:2606.10468v2 Announce Type: replace
Abstract: Coastline detection in remotely sensed imagery is commonly formulated as pixel-wise segmentation, even though coastlines used in coastal monitoring...
By Rafia Malik, Bernhard Pfahringer, Karin Bryan, Mark Dickson, Eibe Frank
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
By Liyao Wang, Ruipu Wu, Haojun Xu, Lei Shi, Linjiang Huang, Si Liu
arXiv:2609.15795v2 Announce Type: replace
Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental...
By Mingkai Liu, Hao Zhao, Xingxing Zuo
arXiv:2609.15795v1 Announce Type: new
Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issu...
By Mingkai Liu, Hao Zhao, Xingxing Zuo
arXiv:2604.13183v4 Announce Type: replace
Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
By Hongyang Zhang, Yinhao Liu, Haitao Zhang, Zhongyi Wen, Zhenyu Kuang, Shuxian Liang, Xian-Sheng Hua
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...
By Chandrakanth Gudavalli, Tajuddin Manhar Mohammed, Abhay Yadav, Ananth Vishnu Bhaskar, Hardik Prajapati, Cheng Peng, Rama Chellappa, Shivkumar Chandrasekaran, B. S. Manjunath
The paper introduces a method that combines large language models (LLMs) with LiDAR geometry to answer complex spatial questions by grounding targets directly in LiDAR point clouds. It presents the SpatialLiDAR-QA dataset for relational grounding tasks and the SpatialLiDAR-LM model, which aligns LiDAR features with an LLM to retrieve and refine target coordinates. Experiments show significant gains over existing LiDAR–language models and multi‑camera vision‑language models in precise coordinate prediction.
By Byounggun Park, Giyong Moon, Jusung Kim, Soonmin Hwang
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...
By Qingyang Liu, David G Shatwell, Parth Parag Kulkarni, Mubarak Shah
Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent w...
LightLoc++ introduces a sensor‑robust framework for outdoor LiDAR localization that decouples a pretrained backbone from lightweight scene‑specific heads, enabling efficient new‑scene training. The authors present SULID, a synchronized urban multi‑LiDAR dataset featuring 32‑, 64‑, and 128‑beam sensors, which is used to pretrain a backbone via cross‑sensor consistency learning. Additional techniques such as sample classification guidance and redundant sample downsampling reduce regression ambiguity and computational load, achieving state‑of‑the‑art performance with minimal new‑scene training cost.
By Wen Li, Shangshu Yu, Dunqiang Liu, Shaoyang Chen, Qiming Xia, Sheng Ao, Siqi Shen, Chenglu Wen, Cheng Wang
ARC‑Loc introduces a new cross‑view localization method that bypasses heavy Bird’s‑Eye‑View transformations and external depth models. By converting ground keypoints into azimuthal rays on a satellite map and exploiting their convergence at the user’s location, the approach uses a minimal Azimuthal Ray Convergence solver and an ARC loss to directly match ground and satellite images. Experiments on VIGOR and KITTI show that ARC‑Loc achieves competitive accuracy while offering faster, memory‑efficient inference and easy integration with existing frameworks.
By Hyeongsik Kim, Mincheol Kim, Heejoon Moon, Je Hyeong Hong