Karl的博客

本次测评的服务器是BandwagonHost的The DC6 Plan，年付\$49.41USD。

本次测评的服务器是YxVM香港区域的Hong Kong Volume Official Version - Basic，月付\$3.00USD。

本次测评的服务器是wap.ac台湾区域的TW 512 LXC，月付\$1.00USD，年付\$10.00USD。

本次测评的服务器是CLAWCLOUD香港区域的VPS(虚拟专用服务器)-1C/1G/20G/500G，季付\$8.40USD。

本次测评的服务器是wap.ac台湾区域的TW 256M VPS，月付\$1.00USD。

本次测评的服务器是LegendVPS新加坡区域的SG-7K62-1-1G，月付\$2.00USD。

本次测评的服务器是netcup德国区域的VPS piko G11s 12M，年付€10.08EUR。

Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. Existing works utilizing mean shapes often fall short of capturing this variation. To address this issue, we present SecondPose, a novel approach integrating object-specific geometric features with semantic category priors from DINOv2. Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information. These geometric features are then point-aligned with DINOv2 features to establish a consistent object representation under SE(3) transformations, facilitating the mapping from camera space to the pre-defined canonical space, thus further enhancing pose estimation. Extensive experiments on NOCS-REAL275 demonstrate that SecondPose achieves a 12.4% leap forward over the state-of-the-art. Moreover, on a more complex dataset HouseCat6D which provides photometrically challenging objects, SecondPose still surpasses other competitors by a large margin.

本次测评的服务器是LegendVPS新加坡区域的SG-BGP-512M，月付\$0.80USD。

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 appearance features. Extensive experiments on the LM-O and YCB-V datasets demonstrate the effectiveness of our framework.