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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - 6d-diff
T2 - A keypoint diffusion framework for 6d object pose estimation
AU - Xu, Li
AU - Qu, Haoxuan
AU - Cai, Yujun
AU - Liu, Jun
PY - 2024/9/16
Y1 - 2024/9/16
N2 - Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Mean-while, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. Inspired by their denoising capability, we propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance. In our framework, to establish accurate 2D-3D correspondence, we formulate 2D keypoints detection as a reverse diffusion (denoising) process. To facilitate such a denoising process, we design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.
AB - Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Mean-while, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. Inspired by their denoising capability, we propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance. In our framework, to establish accurate 2D-3D correspondence, we formulate 2D keypoints detection as a reverse diffusion (denoising) process. To facilitate such a denoising process, we design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.
U2 - 10.1109/CVPR52733.2024.00924
DO - 10.1109/CVPR52733.2024.00924
M3 - Conference contribution/Paper
BT - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
ER -