activity
20142024
most citedKernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

259 citations · 346 across the 25 of their papers we have counts for

collaborators

22 papers

cs.CV2023

RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery

Jiaxin Wei, Xibin Song, Weizhe Liu +3

While showing promising results, recent RGB-D camera-based category-level object pose estimation methods have restricted applications due to the heavy reliance on depth sensors. RG…

cs.CV20237 cited

ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion

Jiayu Yang, Ziang Cheng, Yunfei Duan +2

Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured…

cs.CV20231 cited

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

Xiaoxiao Sun, Nidham Gazagnadou, Vivek Sharma +3

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images…

cs.CV2023

Deep Video Restoration for Under-Display Camera

Xuanxi Chen, Tao Wang, Ziqian Shao +6

Images or videos captured by the Under-Display Camera (UDC) suffer from severe degradation, such as saturation degeneration and color shift. While restoration for UDC has been a cr…

cs.CV2023

Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality

Ziang Cheng, Jiayu Yang, Hongdong Li

Real-time Stereo Matching is a cornerstone algorithm for many Extended Reality (XR) applications, such as indoor 3D understanding, video pass-through, and mixed-reality games. Desp…

cs.CV202310 cited

MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing

Yuwei Qiu, Kaihao Zhang, Chenxi Wang +3

In recent years, Transformer networks are beginning to replace pure convolutional neural networks (CNNs) in the field of computer vision due to their global receptive field and ada…