6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2401.00029v3
• Date: Fri, 22 Mar 2024 07:52:28 GMT
• Title: 6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
• Authors: Li Xu, Haoxuan Qu, Yujun Cai, Jun Liu,
• Abstract summary: Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy. We propose a novel diffusion-based framework to handle the noise and indeterminacy in object pose estimation.
• Score: 16.242361975225066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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 features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.

Related papers

• NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation [86.7260950382448]
  We propose a novel approach to correct noise for image validity, NoiseDiffusion. NoiseDiffusion performs within the noisy image space and injects raw images into these noisy counterparts to address the challenge of information loss.
  arXiv  Detail & Related papers  (2024-03-13T12:32:25Z)
• 3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion [6.914463996768285]
  3DifFusionDet structures 3D object detection as a denoising diffusion process from noisy 3D boxes to target boxes. Under the feature align strategy, the progressive refinement method could make a significant contribution to robust LiDAR-Camera fusion. Experiments on KITTI, a benchmark for real-world traffic object identification, revealed that 3DifFusionDet is able to perform favorably in comparison to earlier, well-respected detectors.
  arXiv  Detail & Related papers  (2023-11-07T05:53:09Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation [66.16525145765604]
  We introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud. Experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.
  arXiv  Detail & Related papers  (2023-10-26T12:47:26Z)
• Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
  We present a novel strategy called the Diffusion Model for Image Denoising (DMID) Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
  arXiv  Detail & Related papers  (2023-07-08T14:59:41Z)
• Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) [9.720777218103052]
  6D object pose estimation from single RGB images presents a significant challenge. We introduce a novel score-based diffusion method applied to the $SE(3)$ group. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity and mitigating perspective-induced ambiguity.
  arXiv  Detail & Related papers  (2023-05-25T09:09:32Z)
• Real-World Denoising via Diffusion Model [14.722529440511446]
  Real-world image denoising aims to recover clean images from noisy images captured in natural environments. diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. This paper proposes a novel general denoising diffusion model that can be used for real-world image denoising.
  arXiv  Detail & Related papers  (2023-05-08T04:48:03Z)
• Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features [17.920305227880245]
  We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of the neural feature activations at each mesh. Our experiments demonstrate an enhanced category-level 6D pose estimation performance compared to prior work.
  arXiv  Detail & Related papers  (2022-09-12T21:31:36Z)
• RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization [46.144194562841435]
  We propose a framework based on a recurrent neural network (RNN) for object pose refinement. The problem is formulated as a non-linear least squares problem based on the estimated correspondence field. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover accurate object poses.
  arXiv  Detail & Related papers  (2022-03-24T06:24:55Z)
• SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation [98.83762558394345]
  SO-Pose is a framework for regressing all 6 degrees-of-freedom (6DoF) for the object pose in a cluttered environment from a single RGB image. We introduce a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects. Cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness.
  arXiv  Detail & Related papers  (2021-08-18T19:49:29Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.