Search Results for author: Gu Wang

Found 10 papers, 6 papers with code

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

no code implementations25 Feb 2023 Martin Sundermeyer, Tomas Hodan, Yann Labbe, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas

In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56. 9 AR$_C$ in 2019 (Vidal et al.) and 69. 8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83. 7 AR$_C$ (GDRNPP).

2D object detection 6D Pose Estimation using RGB +2

CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement

1 code implementation17 Jul 2022 Xingyu Liu, Gu Wang, Yi Li, Xiangyang Ji

While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc.

Pose Estimation

SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

2 code implementations ICCV 2021 Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, Federico Tombari

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.

6D Pose Estimation 6D Pose Estimation using RGB +1

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

1 code implementation CVPR 2021 Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji

In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.

6D Pose Estimation 6D Pose Estimation using RGB +1

DeepIM: Deep Iterative Matching for 6D Pose Estimation

2 code implementations ECCV 2018 Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.

6D Pose Estimation 6D Pose Estimation using RGB +1

Action Recognition with Joint Attention on Multi-Level Deep Features

no code implementations9 Jul 2016 Jialin Wu, Gu Wang, Wukui Yang, Xiangyang Ji

We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

Action Recognition In Videos Temporal Action Localization +1

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