mlsanityData to 7 Oct 2026

DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation

arXiv 2112.08775 ↗First posted 16 Dec 2021cs.CVcited by 9

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Abstract (arXiv)

Predicting the object's 6D pose from a single RGB image is a fundamental computer vision task. Generally, the distance between transformed object vertices is employed as an objective function for pose estimation methods. However, projective geometry in the camera space is not considered in those methods and causes performance degradation. In this regard, we propose a new pose estimation system based on a projective grid instead of object vertices. Our pose estimation method, dynamic projective spatial transformer network (DProST), localizes the region of interest grid on the rays in camera space and transforms the grid to object space by estimated pose. The transformed grid is used as both a sampling grid and a new criterion of the estimated pose. Additionally, because DProST does not require object vertices, our method can be used in a mesh-less setting by replacing the mesh with a reconstructed feature. Experimental results show that mesh-less DProST outperforms the state-of-the-art mesh-based methods on the LINEMOD and LINEMOD-OCCLUSION dataset, and shows competitive performance on the YCBV dataset with mesh data. The source code is available at https://github.com/parkjaewoo0611/DProST

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 37 of its 46 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

37 of 46 references.

References · 37

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25 more
  1. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 490
  2. 2018DeepVoxels: Learning Persistent 3D Feature Embeddingscited by 401
  3. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  4. 2020INeRF: Inverting Neural Radiance Fields for Pose Estimationcited by 381
  5. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  6. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  7. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  8. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
  9. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  10. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  11. 2020BOP Challenge 2020 on 6D Object Localizationcited by 192
  12. 2019DPOD: 6D Pose Object Detector and Refinercited by 189
  13. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
  14. 2020HybridPose: 6D Object Pose Estimation under Hybrid Representationscited by 107
  15. 2018Segmentation-driven 6D Object Pose Estimationcited by 104
  16. 2020Category Level Object Pose Estimation via Neural Analysis-by-Synthesiscited by 100
  17. 2019Single-Stage 6D Object Pose Estimationcited by 88
  18. 2019LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimationcited by 88
  19. 2021SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimationcited by 88
  20. 2018Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimationcited by 74
  21. 2021RePOSE: Fast 6D Object Pose Refinement via Deep Texture Renderingcited by 63
  22. 2018RenderNet: A deep convolutional network for differentiable rendering from 3D shapescited by 42
  23. 2020Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registrationcited by 21
  24. 2020Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Imagescited by 14
  25. 20196D Pose Estimation with Correlation Fusioncited by 6

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3; 2025: 3.

9 papers.

  1. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 39
  2. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  3. 2024MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlationcited by 11
  4. 2023Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimationcited by 6
  5. 2025GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimationcited by 5
  6. 2023Open Challenges for Monocular Single-shot 6D Object Pose Estimationcited by 2
  7. 2024Leveraging Positional Encoding for Robust Multi-Reference-Based Object 6D Pose Estimationcited by 1
  8. 2025RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objectscited by 1
  9. 2025AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generationcited by 0