Research map / 3D vision / Human pose estimation
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
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Formulates 2D keypoint detection for 6D object pose estimation as a reverse diffusion process that starts from a Mixture-of-Cauchy distribution built from heatmaps and is conditioned on appearance features.
Problem
RGB-based 6D object pose estimation suffers from noise and indeterminacy in 2D keypoint detection caused by occlusion and clutter.
Approach
Instead of starting diffusion from Gaussian noise, the reverse process starts from a Mixture-of-Cauchy distribution fitted to keypoint heatmaps from an initializer, with a MoC-based forward process generating supervision and object appearance features as conditioning in a transformer encoder-decoder.
Key results
- Achieves 79.6 mean ADD(-S) on LM-O, the best among compared methods (CheckerPose 77.5, ZebraPose 76.9).
- On YCB-V achieves 83.8 ADD(-S) and 87.0 AUC of ADD(-S), best on both metrics reported; AUC of ADD-S 91.5 is comparable to CheckerPose 91.3.
- Ablation: removing the diffusion model (Variant A) drops LM-O ADD(-S) to 49.2; replacing MoC start with standard Gaussian diffusion gives 73.1.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use 6D-Diff instead of CheckerPose | 6D object pose estimation · LM-O | ADD(-S) mean: 77.5 -> 79.6 | Helped |
| use 6D-Diff instead of CheckerPose | 6D object pose estimation · YCB-V | ADD(-S): 81.4 -> 83.8 | Helped |
| use 6D-Diff instead of GDR-Net | 6D object pose estimation · YCB-V | AUC of ADD-S: 91.6 -> 91.5 | Hurt |
| use 6D-Diff instead of CheckerPose | 6D object pose estimation · YCB-V | AUC of ADD(-S): 86.4 -> 87.0 | Helped |
| add the diffusion denoising process rather than predict keypoints directly from heatmaps (ablation) | 6D-Diff · LM-O | ADD(-S): 49.2 -> 79.6 | Helped |
| learn the reverse denoising process rather than predict coordinates directly (ablation) | 6D-Diff · LM-O | ADD(-S): 57.3 -> 79.6 | Helped |
| use the reverse denoising process rather than a deep stacked direct-coordinate network (ablation) | 6D-Diff · LM-O | ADD(-S): 61.1 -> 79.6 | Helped |
| add object appearance features to the diffusion model (ablation) | 6D-Diff · LM-O | ADD(-S): 74.4 -> 79.6 | Helped |
| use the MoC-based forward process instead of standard diffusion from Gaussian noise (ablation) | 6D-Diff · LM-O | ADD(-S): 73.1 -> 79.6 | Helped |
| use the MoC-based forward process instead of Gaussian-noise denoising conditioned on heatmaps (ablation) | 6D-Diff · LM-O | ADD(-S): 76.2 -> 79.6 | Helped |
Limitations
- Relies on an off-the-shelf object detector and a separate keypoint initializer, making it a two-stage pipeline.
- Requires EM fitting of MoC parameters per sample and multiple sampled trajectories (M=5), adding test-time cost.
- Gains over prior methods are modest on YCB-V AUC of ADD-S.
Experimental setup
Datasets: LM-O YCB-V LM pbr images
Benchmarks: LM-O YCB-V
Metrics: ADD(-S) AUC of ADD-S AUC of ADD(-S)
Baselines: PVNet HybridPose RePose DeepIM GDR-Net SO-Pose CRT-6D ZebraPose CheckerPose SegDriven SingleStage CosyPose
Builds on: DDPM DDIM PVNet ZebraPose CDPNv2
Closest papers by meaning
- Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) (CVPR 2024 · similarity 0.92)
- Object Pose Estimation via the Aggregation of Diffusion Features (CVPR 2024 · similarity 0.88)
- MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models (CVPR 2024 · similarity 0.87)
- HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud (CVPR 2024 · similarity 0.86)
- Any6D: Model-free 6D Pose Estimation of Novel Objects (CVPR 2025 · similarity 0.85)
- DiffuBox: Refining 3D Object Detection with Point Diffusion (NeurIPS 2024 · similarity 0.85)
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