title: UniDet3D: Multi-dataset Indoor 3D Object Detection
summary: Growing customer demand for smart solutions in robotics and augmented reality
  has attracted considerable attention to 3D object detection from point clouds.
  Yet, existing indoor datasets taken individually are too small and
  insufficiently diverse to train a powerful and general 3D object detection
  model. In the meantime, more general approaches utilizing foundation models are
  still inferior in quality to those based on supervised training for a specific
  task. In this work, we propose , a simple yet effective 3D object
  detection model, which is trained on a mixture of indoor datasets and is
  capable of working in various indoor environments. By unifying different label
  spaces,  enables learning a strong representation across multiple
  datasets through a supervised joint training scheme. The proposed network
  architecture is built upon a vanilla transformer encoder, making it easy to
  run, customize and extend the prediction pipeline for practical use. Extensive
  experiments demonstrate that  obtains significant gains over existing 3D
  object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50),
  ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan
  (+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at
  https://github.com/filapro/unidet3d .
paper_authors._id: 66e0122023e5f162e7d16505
paper_authors.name: Maksim Kolodiazhnyi
paper_authors.status: claimed_verified
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paper_authors.fullname: max
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paper_authors.user: maksimko123
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paper_authors.name: Anna Vorontsova
paper_authors._id: 66e0122023e5f162e7d16507
paper_authors.name: Matvey Skripkin
paper_authors.status: admin_assigned
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paper_authors.fullname: Matvey Skripkin
paper_authors.type: user
paper_authors.user: barracuda049
paper_authors._id: 66e0122023e5f162e7d16508
paper_authors.name: Danila Rukhovich
paper_authors.status: admin_assigned
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paper_authors._id: 6394434e872e49c02f5934c3
paper_authors.avatarUrl: /avatars/d77228347a45868e7984607b18a98c29.svg
paper_authors.fullname: Danila Rukhovich
paper_authors.type: user
paper_authors.user: filapro
paper_authors._id: 66e0122023e5f162e7d16509
paper_authors.name: Anton Konushin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-10T09:42:07.787Z
paper_authors._id: 63aae7456ed74f08b7b061cb
paper_authors.avatarUrl: /avatars/664e8cdebb49935c671fda0d65c0407a.svg
paper_authors.fullname: Anton Konushin
paper_authors.type: user
paper_authors.user: ktoshik
paper_title: UniDet3D: Multi-dataset Indoor 3D Object Detection
paper_summary: Growing customer demand for smart solutions in robotics and augmented reality
  has attracted considerable attention to 3D object detection from point clouds.
  Yet, existing indoor datasets taken individually are too small and
  insufficiently diverse to train a powerful and general 3D object detection
  model. In the meantime, more general approaches utilizing foundation models are
  still inferior in quality to those based on supervised training for a specific
  task. In this work, we propose , a simple yet effective 3D object
  detection model, which is trained on a mixture of indoor datasets and is
  capable of working in various indoor environments. By unifying different label
  spaces,  enables learning a strong representation across multiple
  datasets through a supervised joint training scheme. The proposed network
  architecture is built upon a vanilla transformer encoder, making it easy to
  run, customize and extend the prediction pipeline for practical use. Extensive
  experiments demonstrate that  obtains significant gains over existing 3D
  object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50),
  ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan
  (+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at
  https://github.com/filapro/unidet3d .
paper_ai_summary: A 3D object detection model trained on a combined indoor dataset achieves superior performance across multiple benchmarks using a unified label space and transformer encoder architecture.
paper_ai_keywords: 3D object detection point clouds indoor datasets foundation models supervised training label spaces transformer encoder mAP50 mAP25 ScanNet ARKitScenes S3DIS MultiScan 3RScan ScanNet++