Estimating 2D-3D correspondences between RGB images and 3D space is a
fundamental problem in 6D object pose estimation. Recent pose estimators use
dense correspondence maps and Point-to-Point algorithms to estimate object
poses. The accuracy of pose estimation depends heavily on the quality of the
dense correspondence maps and their ability to withstand occlusion, clutter,
and challenging material properties. Currently, dense correspondence maps are
estimated using image-to-image translation models based on GANs, Autoencoders,
or direct regression models. However, recent advancements in image-to-image
translation have led to diffusion models being the superior choice when
evaluated on benchmarking datasets. In this study, we compare image-to-image
translation networks based on GANs and diffusion models for the downstream task
of 6D object pose estimation. Our results demonstrate that the diffusion-based
image-to-image translation model outperforms the GAN, revealing potential for
further improvements in 6D object pose estimation models.

比较了基于 GAN 和扩散模型的图像翻译网络在 6D 物体姿态估计任务中的表现，结果表明扩散模型优于 GAN，揭示了进一步改进物体姿态估计模型的潜力。

利用扩散模型改进 2D-3D 稠密对应关系以提高 6D 物体姿态估计

Improving 2D-3D Dense Correspondences with Diffusion Models for 6D  Object Pose Estimation

Human mesh reconstruction from a single image is challenging in the presence
of occlusion, which can be caused by self, objects, or other humans. Existing
methods either fail to separate human features accurately or lack proper
supervision for feature completion. In this paper, we propose Dense Inpainting
Human Mesh Recovery (DIMR), a two-stage method that leverages dense
correspondence maps to handle occlusion. Our method utilizes a dense
correspondence map to separate visible human features and completes human
features on a structured UV map dense human with an attention-based feature
completion module. We also design a feature inpainting training procedure that
guides the network to learn from unoccluded features. We evaluate our method on
several datasets and demonstrate its superior performance under heavily
occluded scenarios compared to other methods. Extensive experiments show that
our method obviously outperforms prior SOTA methods on heavily occluded images
and achieves comparable results on the standard benchmarks (3DPW).

我们提出了一种名为 Dense Inpainting Human Mesh Recovery (DIMR) 的两阶段方法，利用密集的对应关系图处理遮挡问题，采用基于注意力机制的特征完成模块，通过特征修补训练过程指导网络从非遮挡特征中学习，实验证明我们的方法在重度遮挡场景下表现优异，并在标准基准（3DPW）上取得可比较的结果。