arXiv AI
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Aug 21
UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM
arXiv:2601. 11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain.
arXiv Computer Vision
Sep 1
InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization
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...
arXiv Machine Learning
Aug 11
SIP: Site in Pieces- A Dataset of Disaggregated Construction-Phase 3D Scans for Semantic Segmentation and Scene Understanding
arXiv:2512. 09062v2 Announce Type: replace-cross Abstract: Accurate 3D scene interpretation in active construction sites is essential for progress monitoring, safety assessment, and digital twin development.
arXiv Computer Vision
Aug 28
GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment
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.
arXiv AI
Aug 21
Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training
arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.
arXiv AI
Jul 28
UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation
arXiv:2602. 19349v2 Announce Type: replace-cross Abstract: LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode.