Rigidity-Aware Detection for 6D Object Pose Estimation

Hai, Yang; Song, Rui; Li, Jiaojiao; Salzmann, Mathieu; Hu, Yinlin

doi:10.1109/cvpr52729.2023.00862

Cited by 8 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in complex scenes with a poor detection performance, the estimation results of these methods will be greatly affected. To address this issue, Hai et al [113] introduce a rigidity-aware detection method. This innovative approach capitalizes on the inherent rigidity property of the task object and formulates bounding boxes by sampling from the visible region rather than including the occluded part.…”

Section: Other Methodsmentioning

confidence: 99%

A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios

Guan,

Hao,

et al. 2024

Sensors

View full text Add to dashboard Cite

Recently, 6DoF object pose estimation has become increasingly important for a broad range of applications in the fields of virtual reality, augmented reality, autonomous driving, and robotic operations. This task involves extracting the target area from the input data and subsequently determining the position and orientation of the objects. In recent years, many new advances have been made in pose estimation. However, existing reviews have the problem of only summarizing category-level or instance-level methods, and not comprehensively summarizing deep learning methods. This paper will provide a comprehensive review of the latest progress in 6D pose estimation to help researchers better understanding this area. In this study, the current methods about 6DoF object pose estimation are mainly categorized into two groups: instance-level and category-level groups, based on whether it is necessary to acquire the CAD model of the object. Recent advancements about learning-based 6DoF pose estimation methods are comprehensively reviewed. The study systematically explores the innovations and applicable scenarios of various methods. It provides an overview of widely used datasets, task metrics, and diverse application scenarios. Furthermore, state-of-the-art methods are compared across publicly accessible datasets, taking into account differences in input data types. Finally, we summarize the challenges of current tasks, methods for different applications, and future development directions.

show abstract

Section: Other Methodsmentioning

confidence: 99%

A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios

Guan,

Hao,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Pseudo-Keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation

Wu,

Greenspan

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Object Pose Estimation Using Color Images and Predicted Depth Maps

Hoang,

Xuan Tan,

Nguyen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The task of object pose estimation in computer vision heavily relies on both color (RGB) and depth (D) images to provide crucial appearance and geometric information, assisting algorithms in understanding occlusions and object geometry, thereby enhancing accuracy. However, the dependency on specialized sensors capable of capturing depth poses challenges in terms of cost and availability. Consequently, researchers are exploring methods to estimate object poses solely from RGB images. Nevertheless, this approach encounters difficulties in handling occlusions, discerning object geometry, and resolving ambiguities arising from similar color or texture patterns. This paper introduces a novel geometry-aware method for object pose estimation utilizing RGB images as input to determine the poses of multiple object instances. Our approach leverages both depth and color images during training but only relies on color images during inference. Departing from traditional depth sensors, our method computes predicted point clouds directly from estimated depth images derived from RGB inputs. A key innovation lies in the formulation of a multi-scale fusion module adept at seamlessly integrating features extracted from RGB images with those inferred from the predicted point clouds. This fusion process significantly fortifies the pose estimation pipeline by harnessing the strengths of both modalities, resulting in notably improved object poses. Extensive experimentation demonstrates that our approach markedly outperforms state-of-the-art RGB-based methods on Occluded-LINEMOD and YCB-Video datasets. Moreover, our method achieves competitive results compared to RGB-D approaches that necessitate both RGB and depth data from physical sensors.

show abstract

Rigidity-Aware Detection for 6D Object Pose Estimation

Cited by 8 publications

References 38 publications

A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios

A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios

Pseudo-Keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation

Object Pose Estimation Using Color Images and Predicted Depth Maps

Contact Info

Product

Resources

About