mlsanityData to 7 Oct 2026

T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression

arXiv 2109.10948 ↗First posted 22 Sep 2021cs.CVcited by 16

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Abstract (arXiv)

6D pose estimation is the task of predicting the translation and orientation of objects in a given input image, which is a crucial prerequisite for many robotics and augmented reality applications. Lately, the Transformer Network architecture, equipped with a multi-head self-attention mechanism, is emerging to achieve state-of-the-art results in many computer vision tasks. DETR, a Transformer-based model, formulated object detection as a set prediction problem and achieved impressive results without standard components like region of interest pooling, non-maximal suppression, and bounding box proposals. In this work, we propose T6D-Direct, a real-time single-stage direct method with a transformer-based architecture built on DETR to perform 6D multi-object pose direct estimation. We evaluate the performance of our method on the YCB-Video dataset. Our method achieves the fastest inference time, and the pose estimation accuracy is comparable to state-of-the-art methods.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 32 of its 37 references that are in the map. First posted on arXiv 22 Sep 2021.

Builds on

32 of 37 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  8. 2015Fast R-CNNcited by 2,930
  9. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  10. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  11. 2021Transformers in Vision: A Surveycited by 1,009
  12. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
20 more
  1. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  2. 2020Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentationcited by 326
  3. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  4. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  5. 2017SSD-6D: Making RGB-based 3D detection and 6D pose estimation great againcited by 243
  6. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  7. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  8. 2020BOP Challenge 2020 on 6D Object Localizationcited by 192
  9. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  10. 2021GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimatorcited by 148
  11. 2017Learning non-maximum suppressioncited by 112
  12. 2018Segmentation-driven 6D Object Pose Estimationcited by 104
  13. 2019Single-Stage 6D Object Pose Estimationcited by 88
  14. 2020Self6D: Self-Supervised Monocular 6D Object Pose Estimationcited by 79
  15. 2018Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimationcited by 74
  16. 2018Deep Model-Based 6D Pose Refinement in RGBcited by 59
  17. 2021PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimationcited by 14
  18. 2018Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packingcited by 12
  19. 2019Refining 6D Object Pose Predictions using Abstract Render-and-Comparecited by 8
  20. 2018Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networkscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 4; 2025: 3; 2026: 1.

16 papers.

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  1. 2021SPViT: Enabling Faster Vision Transformers via Soft Token Pruningcited by 165
  2. 2022Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Trainingcited by 24
  3. 2024LPViT: Low-Power Semi-structured Pruning for Vision Transformerscited by 15
  4. 2022PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimationcited by 13
  5. 2024Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenescited by 12
  6. 2024A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationshipscited by 11
  7. 2023CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTscited by 9
  8. 2023TransPose: 6D Object Pose Estimation with Geometry-Aware Transformercited by 7
  9. 2022Video based Object 6D Pose Estimation using Transformerscited by 5
  10. 2024RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Imagescited by 5
  11. 2025Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Framescited by 3
  12. 2022Review on 6D Object Pose Estimation with the focus on Indoor Scene Understandingcited by 0
4 more
  1. 2023TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinementcited by 0
  2. 2025MVTOP: Multi-View Transformer-based Object Pose-Estimationcited by 0
  3. 2025PoseStreamer: A Multi-modal Framework for 3D Tracking of Unseen Moving Objectscited by 0
  4. 2026TinyDETR-Pose: Towards End-to-End Real-Time Single-Stage 6DoF Object Pose Estimation with Lightweight Transformerscited by 0