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20232026
most citedCollaborative Visual Place Recognition

4 citations · 6 across the 8 of their papers we have counts for

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9 papers · 1 filter

cs.CV2026

SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Video

Howard Huang, Bharath Surianarayanan, Keifer Lee +2

Precise 3D representations of industrial environments enable tasks such as robot localization and digital twin generation. We propose SAVMap, a method for generating a semantic wir…

cs.CV2024

NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments

Taiyi Pan, Junyang He, Chao Chen +2

Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM). VPR…

cs.CV20241 cited

Tell Me Where You Are: Multimodal LLMs Meet Place Recognition

Zonglin Lyu, Juexiao Zhang, Mingxuan Lu +2

Large language models (LLMs) exhibit a variety of promising capabilities in robotics, including long-horizon planning and commonsense reasoning. However, their performance in place…

cs.CV2024

Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset

Yiming Li, Zhiheng Li, Nuo Chen +5

Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of…

cs.CV2024

Memorize What Matters: Emergent Scene Decomposition from Multitraverse

Yiming Li, Zehong Wang, Yue Wang +5

Humans naturally retain memories of permanent elements, while ephemeral moments often slip through the cracks of memory. This selective retention is crucial for robotic perception,…

cs.CV2024

ActFormer: Scalable Collaborative Perception via Active Queries

Suozhi Huang, Juexiao Zhang, Yiming Li +1

Collaborative perception leverages rich visual observations from multiple robots to extend a single robot's perception ability beyond its field of view. Many prior works receive me…