Object pose using diffusion denoising to detect 2D keypoints for 2D-3D map with 6D-Diff
Object pose using diffusion denoising to detect 2D keypoints for 2D-3D map with 6D-Diff
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2401.00029
arXiv PDF paper https://arxiv.org/pdf/2401.00029.pdf
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
Meanwhile, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising.
… propose a novel diffusion-based framework (6D-Diff) to handle the noise and indeterminacy in object pose estimation for better performance.
… to establish accurate 2D-3D correspondence, … formulate 2D keypoints detection as a reverse diffusion (denoising) process.
To facilitate … denoising … design a Mixture-of-Cauchy-based forward diffusion process and condition the reverse process on the object features.
Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of … framework.
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