arXiv Computer Vision
Oct 8

LiG-DETR: Local-in-Global Reassembly in Latent Space for Aerial Object Detection

LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.

By Yupeng Zhang, Fangzhuo Gao, Juntao Cheng, Ziyi Zhao, Liang Wan, Ruize Han
arXiv Computer Vision
Sep 22

M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests

M3GA-Wild is a new benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests, combining synchronized RGB imagery and LiDAR from ground traversals with high‑resolution aerial imagery and multi‑altitude LiDAR over 370 hectares. The dataset includes accurate geo‑referenced 6‑DoF poses and spans 36 km of forest traversals, enabling systematic evaluation of visual, LiDAR, cross‑modal, and multi‑modal methods. Baseline experiments show LiDAR outperforms vision‑only approaches under severe viewpoint changes, while current multi‑modal fusion offers limited gains due to poor cross‑modal alignment, highlighting challenges in cross‑platform localisation and domain gaps.

By Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, Milad Ramezani