mlsanityData to 7 Oct 2026

Category-Level Metric Scale Object Shape and Pose Estimation

arXiv 2109.00326 ↗First posted 1 Sep 2021cs.CV cs.ROcited by 40

Not on the map. Why?

Abstract (arXiv)

Advances in deep learning recognition have led to accurate object detection with 2D images. However, these 2D perception methods are insufficient for complete 3D world information. Concurrently, advanced 3D shape estimation approaches focus on the shape itself, without considering metric scale. These methods cannot determine the accurate location and orientation of objects. To tackle this problem, we propose a framework that jointly estimates a metric scale shape and pose from a single RGB image. Our framework has two branches: the Metric Scale Object Shape branch (MSOS) and the Normalized Object Coordinate Space branch (NOCS). The MSOS branch estimates the metric scale shape observed in the camera coordinates. The NOCS branch predicts the normalized object coordinate space (NOCS) map and performs similarity transformation with the rendered depth map from a predicted metric scale mesh to obtain 6d pose and size. Additionally, we introduce the Normalized Object Center Estimation (NOCE) to estimate the geometrically aligned distance from the camera to the object center. We validated our method on both synthetic and real-world datasets to evaluate category-level object pose and shape.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

25 of 33 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  3. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  4. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  5. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  6. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  7. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 490
  8. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  9. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
  10. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  11. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  12. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
13 more
  1. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  2. 2019DPOD: 6D Pose Object Detector and Refinercited by 189
  3. 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
  4. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 158
  5. 2019Point-Based Multi-View Stereo Networkcited by 147
  6. 2016Generative and Discriminative Voxel Modeling with Convolutional Neural Networkscited by 128
  7. 2020Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimationcited by 123
  8. 2019Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networkscited by 106
  9. 2020Category Level Object Pose Estimation via Neural Analysis-by-Synthesiscited by 100
  10. 2020Self6D: Self-Supervised Monocular 6D Object Pose Estimationcited by 79
  11. 2017DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Imagecited by 25
  12. 2017Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challengecited by 19
  13. 20183D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Imagecited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 7; 2023: 9; 2024: 8; 2025: 11; 2026: 3.

40 papers.

Sort:

By area:

  1. 2022OnePose: One-Shot Object Pose Estimation without CAD Modelscited by 155
  2. 2023OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Modelscited by 114
  3. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 70
  4. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 69
  5. 2025Any6D: Model-free 6D Pose Estimation of Novel Objectscited by 47
  6. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 39
  7. 2023TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimationcited by 32
  8. 2022Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Imagecited by 29
  9. 2021UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimationcited by 25
  10. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  11. 2021ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimationcited by 20
  12. 2023FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objectscited by 18
28 more
  1. 2022Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wildcited by 17
  2. 2023RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recoverycited by 13
  3. 2024LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimationcited by 13
  4. 2023MFOS: Model-Free & One-Shot Object Pose Estimationcited by 10
  5. 2024OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimationcited by 10
  6. 2024CRISP: Object Pose and Shape Estimation with Test-Time Adaptationcited by 9
  7. 2022One-Shot Neural Fields for 3D Object Understandingcited by 5
  8. 2025GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimationcited by 5
  9. 2025MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Modelcited by 4
  10. 2024Marrying NeRF with Feature Matching for One-step Pose Estimationcited by 3
  11. 2025Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypescited by 3
  12. 2022On the Evaluation of RGB-D-based Categorical Pose and Shape Estimationcited by 2
  13. 2023RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluationcited by 2
  14. 2025DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulationcited by 2
  15. 2026Event6D: Event-based Novel Object 6D Pose Trackingcited by 2
  16. 2023Category-level Shape Estimation for Densely Cluttered Objectscited by 1
  17. 2023MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimationcited by 1
  18. 2024ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depthcited by 1
  19. 2025You Only Pose Once: A Minimalist's Detection Transformer for Monocular RGB Category-level 9D Multi-Object Pose Estimationcited by 1
  20. 2024Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Referencecited by 0
  21. 2024Category Level 6D Object Pose Estimation from a Single RGB Image using Diffusioncited by 0
  22. 2025Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstructioncited by 0
  23. 2025Rethinking Rotation-Invariant Recognition of Fine-grained Shapes from the Perspective of Contour Pointscited by 0
  24. 2025Category-Level and Open-Set Object Pose Estimation for Roboticscited by 0
  25. 2025RCGNet: RGB-based Category-Level 6D Object Pose Estimation with Geometric Guidancecited by 0
  26. 2025Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimationcited by 0
  27. 2026Object Pose Transformer: Unifying Unseen Object Pose Estimationcited by 0
  28. 2026Emergence of a Shared Canonical Object Frame from In-the-Wild Videoscited by 0