title: OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects
summary: We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized
  Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box
  annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object
  classes and 200 times more instances than existing NOCS datasets (NOCS-Real275,
  Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS
  prediction model (NOCSformer) that can predict accurate NOCS, instance masks
  and poses from 2D object detections across diverse classes. It is the first
  NOCS model that can generalize to a broad range of classes when prompted with
  2D boxes. We evaluate our model on the task of 3D oriented bounding box
  prediction, where it achieves comparable results to state-of-the-art 3D
  detection methods such as Cube R-CNN. Unlike other 3D detection methods, our
  model also provides detailed and accurate 3D object shape and segmentation. We
  propose a novel benchmark for the task of NOCS prediction based on OmniNOCS,
  which we hope will serve as a useful baseline for future work in this area. Our
  dataset and code will be at the project website: https://omninocs.github.io.
paper_authors._id: 669092a6cbcaf7ab0ea43eba
paper_authors.name: Akshay Krishnan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:42:29.825Z
paper_authors._id: 65a80d48fa00b7c14657161c
paper_authors.avatarUrl: /avatars/28d42d6228338ce61544fbfee3ea7fc2.svg
paper_authors.fullname: Akshay Krishnan
paper_authors.type: user
paper_authors.user: AkshayKrishnan
paper_authors._id: 669092a6cbcaf7ab0ea43ebb
paper_authors.name: Abhijit Kundu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:42:35.766Z
paper_authors._id: 660adfb5c50f8393c53d7b6f
paper_authors.avatarUrl: /avatars/227dd50a33ca93dd295cb5463eeaa96d.svg
paper_authors.fullname: Abhijit Kundu
paper_authors.type: user
paper_authors.user: abhijitstat
paper_authors._id: 669092a6cbcaf7ab0ea43ebc
paper_authors.name: Kevis-Kokitsi Maninis
paper_authors._id: 669092a6cbcaf7ab0ea43ebd
paper_authors.name: James Hays
paper_authors._id: 669092a6cbcaf7ab0ea43ebe
paper_authors.name: Matthew Brown
paper_title: OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects
paper_summary: We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized
  Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box
  annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object
  classes and 200 times more instances than existing NOCS datasets (NOCS-Real275,
  Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS
  prediction model (NOCSformer) that can predict accurate NOCS, instance masks
  and poses from 2D object detections across diverse classes. It is the first
  NOCS model that can generalize to a broad range of classes when prompted with
  2D boxes. We evaluate our model on the task of 3D oriented bounding box
  prediction, where it achieves comparable results to state-of-the-art 3D
  detection methods such as Cube R-CNN. Unlike other 3D detection methods, our
  model also provides detailed and accurate 3D object shape and segmentation. We
  propose a novel benchmark for the task of NOCS prediction based on OmniNOCS,
  which we hope will serve as a useful baseline for future work in this area. Our
  dataset and code will be at the project website: https://omninocs.github.io.
paper_ai_summary: A transformer-based model, NOCSformer, achieves state-of-the-art performance in predicting 3D NOCS, masks, and poses from 2D detections using a large-scale NOCS dataset.
paper_ai_keywords: monocular dataset NOCS maps object masks 3D bounding box annotations transformer-based model NOCSformer 3D oriented bounding box prediction Cube R-CNN