title: Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using
    Large-scale Public Data
summary: Top-down Bird's Eye View (BEV) maps are a popular representation for ground
  robot navigation due to their richness and flexibility for downstream tasks.
  While recent methods have shown promise for predicting BEV maps from
  First-Person View (FPV) images, their generalizability is limited to small
  regions captured by current autonomous vehicle-based datasets. In this context,
  we show that a more scalable approach towards generalizable map prediction can
  be enabled by using two large-scale crowd-sourced mapping platforms, Mapillary
  for FPV images and OpenStreetMap for BEV semantic maps. We introduce Map It
  Anywhere (MIA), a data engine that enables seamless curation and modeling of
  labeled map prediction data from existing open-source map platforms. Using our
  MIA data engine, we display the ease of automatically collecting a dataset of
  1.2 million pairs of FPV images & BEV maps encompassing diverse geographies,
  landscapes, environmental factors, camera models & capture scenarios. We
  further train a simple camera model-agnostic model on this data for BEV map
  prediction. Extensive evaluations using established benchmarks and our dataset
  show that the data curated by MIA enables effective pretraining for
  generalizable BEV map prediction, with zero-shot performance far exceeding
  baselines trained on existing datasets by 35%. Our analysis highlights the
  promise of using large-scale public maps for developing & testing generalizable
  BEV perception, paving the way for more robust autonomous navigation.
paper_authors._id: 6690dd5027a412c0a0baf1d9
paper_authors.name: Cherie Ho
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paper_authors.fullname: Cherie Ho
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paper_authors.name: Jiaye Zou
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paper_authors.fullname: Jiaye Zou
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paper_authors.user: tonyzzzzzz
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paper_authors.name: Omar Alama
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paper_authors.fullname: Omar Alama
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paper_authors.user: OasisArtisan
paper_authors._id: 6690dd5027a412c0a0baf1dc
paper_authors.name: Sai Mitheran Jagadesh Kumar
paper_authors._id: 6690dd5027a412c0a0baf1dd
paper_authors.name: Benjamin Chiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:45:28.252Z
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paper_authors.fullname: Benjamin Chiang
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paper_authors.user: bchiang2
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paper_authors.name: Taneesh Gupta
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paper_authors._id: 6298d79243e990340bec66c9
paper_authors.avatarUrl: /avatars/90d3d5169c9b9824fa0afc63751cc29f.svg
paper_authors.fullname: Taneesh Gupta
paper_authors.type: user
paper_authors.user: Taneesh12
paper_authors._id: 6690dd5027a412c0a0baf1df
paper_authors.name: Chen Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:46:07.140Z
paper_authors._id: 6514b5b7a1b6d19bd1601311
paper_authors.avatarUrl: /avatars/156277ccca5415d70336afa4fbae0d14.svg
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: chenwang001
paper_authors._id: 6690dd5027a412c0a0baf1e0
paper_authors.name: Nikhil Keetha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:46:37.051Z
paper_authors._id: 64dbacab1855ce11cd61be0f
paper_authors.avatarUrl: /avatars/22f53e8e520067b1053f8f7e21f5fd25.svg
paper_authors.fullname: Nikhil Keetha
paper_authors.type: user
paper_authors.user: NikV09
paper_authors._id: 6690dd5027a412c0a0baf1e1
paper_authors.name: Katia Sycara
paper_authors._id: 6690dd5027a412c0a0baf1e2
paper_authors.name: Sebastian Scherer
paper_title: Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using
    Large-scale Public Data
paper_summary: Top-down Bird's Eye View (BEV) maps are a popular representation for ground
  robot navigation due to their richness and flexibility for downstream tasks.
  While recent methods have shown promise for predicting BEV maps from
  First-Person View (FPV) images, their generalizability is limited to small
  regions captured by current autonomous vehicle-based datasets. In this context,
  we show that a more scalable approach towards generalizable map prediction can
  be enabled by using two large-scale crowd-sourced mapping platforms, Mapillary
  for FPV images and OpenStreetMap for BEV semantic maps. We introduce Map It
  Anywhere (MIA), a data engine that enables seamless curation and modeling of
  labeled map prediction data from existing open-source map platforms. Using our
  MIA data engine, we display the ease of automatically collecting a dataset of
  1.2 million pairs of FPV images & BEV maps encompassing diverse geographies,
  landscapes, environmental factors, camera models & capture scenarios. We
  further train a simple camera model-agnostic model on this data for BEV map
  prediction. Extensive evaluations using established benchmarks and our dataset
  show that the data curated by MIA enables effective pretraining for
  generalizable BEV map prediction, with zero-shot performance far exceeding
  baselines trained on existing datasets by 35%. Our analysis highlights the
  promise of using large-scale public maps for developing & testing generalizable
  BEV perception, paving the way for more robust autonomous navigation.
paper_ai_summary: A data engine, Map It Anywhere (MIA), curates large-scale FPV images and BEV maps, enabling effective pretraining for generalizable BEV map prediction, surpassing existing methods.
paper_ai_keywords: Bird's Eye View (BEV) maps First-Person View (FPV) images crowd-sourced mapping platforms Mapillary OpenStreetMap BEV semantic maps camera model-agnostic model zero-shot performance