EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$
While Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in standard visual understanding, adapting them for active visual search in 360$^\circ$ panoramic environments exposes fundamental limitations. Specifically, standard MLLMs struggle to effectively model inherent panoramic properties, such as severe polar distortion and continuous cylindrical topologies, which significantly degrades target detection accuracy. Consequently, existing panoramic search methods attempt to compensate by relying heavily on fragmented local viewpoints. Burdened by rigid initializa
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorJingtao Xu →
EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$
- Linked via arxiv authorZizhuo Lin →
EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$
- Linked via arxiv authorJianwen Sun →
EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$
- Linked via arxiv authorYi Yang →
EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$
- Linked via arxiv authorYawei Luo →
EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$