GeoMAD is a multi‑view anomaly detection framework that fuses multiple camera viewpoints while maintaining geometric awareness and scalability to multi‑class industrial settings. It introduces a Cross‑view Deformable Fusion Module (CDFM) that learns view‑pair‑specific sampling offsets on 2D feature maps, enabling hierarchical cross‑view correspondence without camera calibration or voxel construction. Additionally, Distributional View Alignment (DVA) provides a self‑supervised loss that aligns bottleneck distributions across views, ensuring global consistency without pixel‑level correspondence. Together, CDFM and DVA achieve geometry‑aware, distribution‑consistent fusion and demonstrate strong detection and localization performance on Real‑IAD and MANTA‑Tiny datasets.
By Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua
Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platforms enable efficient large-area data acquisition, but merging independently constructed local maps in GNSS-denied understory environments still requires initialization-free loop closure detection and global registration.
The paper introduces the Register Anything Model (RAM), a unified approach for 3D point cloud registration that can robustly estimate 6‑DoF transformations across a wide range of domains, including indoor, outdoor, and object scenes. RAM overcomes domain‑specific challenges by employing a geometry‑aware normalization that voxelizes and standardizes point clouds based on their geometric properties, followed by a hierarchical coarse‑to‑fine pipeline with a geometric transformer that focuses on local consistency. Experiments on 12 benchmarks show that RAM achieves state‑of‑the‑art inlier ratios and registration recall, outperforming methods trained on individual domains and demonstrating strong generalization.
By Zheng Qin, Junhua Xi, Yan Huang, Jiahong Lai, Anwen Huang, Qiong Li, Guangda Zhang, Kai Xu
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...
By Angel Daruna, Ben Southall, Niluthpol Chowdhury Mithun, Kshitij Minhas, Nicholas Meegan, Qiao Wang, Bogdan Matei, Supun Samarasekera, Rakesh Kumar
arXiv:2606. 17564v1 Announce Type: cross Abstract: Standardized evaluation protocols are indispensable for robust benchmarking in remote sensing, particularly as foundation features are increasingly transferred across diverse sensors and complex imaging geometries.
By Qiyan Luo, Jie Yang, Yingdong Pi, Lekang Wen, Mi Wang
VolFill is a generative framework that reconstructs the complete 3D geometry of a scene from a single RGB image, addressing the challenge of inferring hidden structures. It uses a hybrid 3D VAE to encode sparse truncated unsigned distance function grids into a latent space, and a latent Diffusion Transformer to denoise and recover the full scene. By conditioning on a geometry foundation model, VolFill leverages spatial priors and provides a structured representation that enables direct surface extraction and scalable occupancy queries, outperforming existing amodal baselines on SCRREAM and NRGB-D datasets.
By Tuan Duc Ngo, Chuang Gan, Evangelos Kalogerakis