We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends existing forest localisation datasets, providing a holistic benchmark with synchronised RGB imagery and LiDAR from ground traversals spanning 36 km, aligned high-resoluti...
Ethan Griffiths, Maryam Haghighat, Simon Denman et al.· 0 citations
TOLiD is proposed, a self-supervised pretraining method for LiDAR representation learning that addresses the gap between dense ViT token representations and sparse 3D encoders by coupling a LiDAR backbone with a student Vision Transformer initialized from a frozen VFM teacher and applying supervision over compatible pa...
Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy et al.· arXiv.org· 1 citation
This paper introduces the first attack that directly optimizes an encoder-attention objective under an imperceptible, bounded, bounded perturbation, and argues that encoder attention concentrates the model's spatial reasoning, so corrupting it propagates through the detection pipeline more disruptively than perturbing...
Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando et al.· 0 citations
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