title: PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes
summary: We introduce the novel task of Language-Guided Object Placement in Real 3D
  Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual
  prompt broadly describing where the 3D asset should be placed. The task here is
  to find a valid placement for the 3D asset that respects the prompt. Compared
  with other language-guided localization tasks in 3D scenes such as grounding,
  this task has specific challenges: it is ambiguous because it has multiple
  valid solutions, and it requires reasoning about 3D geometric relationships and
  free space. We inaugurate this task by proposing a new benchmark and evaluation
  protocol. We also introduce a new dataset for training 3D LLMs on this task, as
  well as the first method to serve as a non-trivial baseline. We believe that
  this challenging task and our new benchmark could become part of the suite of
  benchmarks used to evaluate and compare generalist 3D LLM models.
paper_authors._id: 681d9dd229119d666079b275
paper_authors.name: Ahmed Abdelreheem
paper_authors.status: claimed_verified
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paper_authors.fullname: Ahmed Abdelreheem
paper_authors.type: user
paper_authors.user: Samir55
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paper_authors.name: Filippo Aleotti
paper_authors.status: admin_assigned
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paper_authors.fullname: Filippo Aleotti
paper_authors.type: user
paper_authors.user: Filippo8
paper_authors._id: 681d9dd229119d666079b277
paper_authors.name: Jamie Watson
paper_authors.status: admin_assigned
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paper_authors.fullname: Jamie Watson
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paper_authors.user: Aileron
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paper_authors.name: Zawar Qureshi
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/8042f99c6eb2c9b61be8c9b950818b2f.svg
paper_authors.fullname: Zawar Qureshi
paper_authors.type: user
paper_authors.user: zuluquebec
paper_authors._id: 681d9dd229119d666079b279
paper_authors.name: Abdelrahman Eldesokey
paper_authors.status: admin_assigned
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paper_authors._id: 6577f3bacfb2207f11e847bb
paper_authors.avatarUrl: /avatars/825998cfebc47d8106f633be5ad10964.svg
paper_authors.fullname: Abdelrahman Eldesokey
paper_authors.type: user
paper_authors.user: abdo-eldesokey
paper_authors._id: 681d9dd229119d666079b27a
paper_authors.name: Peter Wonka
paper_authors._id: 681d9dd229119d666079b27b
paper_authors.name: Gabriel Brostow
paper_authors._id: 681d9dd229119d666079b27c
paper_authors.name: Sara Vicente
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T09:05:21.990Z
paper_authors._id: 67f517dcbd75b1099bba2857
paper_authors.avatarUrl: /avatars/a1a25d7972b1857f8bb49bc9efc02ded.svg
paper_authors.fullname: Sara Vicente
paper_authors.type: user
paper_authors.user: svicente
paper_authors._id: 681d9dd229119d666079b27d
paper_authors.name: Guillermo Garcia-Hernando
paper_title: PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes
paper_summary: We introduce the novel task of Language-Guided Object Placement in Real 3D
  Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual
  prompt broadly describing where the 3D asset should be placed. The task here is
  to find a valid placement for the 3D asset that respects the prompt. Compared
  with other language-guided localization tasks in 3D scenes such as grounding,
  this task has specific challenges: it is ambiguous because it has multiple
  valid solutions, and it requires reasoning about 3D geometric relationships and
  free space. We inaugurate this task by proposing a new benchmark and evaluation
  protocol. We also introduce a new dataset for training 3D LLMs on this task, as
  well as the first method to serve as a non-trivial baseline. We believe that
  this challenging task and our new benchmark could become part of the suite of
  benchmarks used to evaluate and compare generalist 3D LLM models.
paper_ai_summary: A new task for placing 3D assets in real 3D scenes based on textual prompts is introduced, with a benchmark and dataset for evaluating 3D LLMs.
paper_ai_keywords: Language-Guided Object Placement point cloud 3D asset textual prompt valid placement 3D geometric relationships free space benchmark dataset 3D LLMs baseline