TP-AE: Temporally Primed 6D Object Pose Tracking with Auto-Encoders

Zheng, Linfang; Leonardis, Aleš; Tse, Tze Ho Elden; Horányi, Nóra; Chen, Hua; Zhang, Wei; Chang, Hyung Jin

doi:10.1109/icra46639.2022.9811890

Cited by 8 publications

(3 citation statements)

References 49 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to those render-and-compare algorithms, PoseRBPF [14] uses a Rao-Blackwellized particle filter and pose-representative latent codes [44]. Also, TP-AE [45] proposed a temporally primed framework with auto encoders, while ROFT [46] synchronizes low framerate pose estimates with fast optical flow.…”

Section: Related Workmentioning

confidence: 99%

A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

Stoiber

Pfanne

Strobl

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multimodality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learningbased methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available.

show abstract

Section: Related Workmentioning

confidence: 99%

A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

Stoiber

Pfanne

Strobl

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…This method is faster, but it often struggles to achieve precise estimation. The second is the template matching method [21][22][23][24], this approach searches for the closest template in a template library to match with the image, estimating object poses. This method is robust for texture-less objects but may have limited applicability in real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

AG-Net: Category-level 6D Pose Estimation Network Based on Attention Mechanism and Global Enhancement

Bowen,

Jinlong,

Suqin

et al. 2024

Preprint

View full text Add to dashboard Cite

How to effectively enhance feature extraction is a challenge faced by current 6D pose estimation methods. To address this issue, we propose a novel 6D pose estimation network based on VI-Net, shortened as AG-Net, which uses ECA block and Global enhancev module to enhance feature extraction: ECA block embeds a channel attention mechanism into the convolutional layers, replacing the fully connected layers with 1×1Conv to capture relationships between different channels and improves the performance of feature extraction. The Global enhancev module further processes the received information and enhances feature extraction by fusing global features, effectively balancing performance and speed, and better estimating the translation and size of objects. We applied the proposed AG-Net to category-level 6D pose estimation tasks and tested it on the REAL275 and CAMER25 datasets using IOU 3D intersection and n◦mcm evaluation metrics. The results showed that AG-Net outperformed current state-of-the-art methods. Our code and models are available at https://github.com/AFESDTTM/AG-Net

show abstract

“…[11,44] use the 3D models as templates, which achieve high accuracy but suffer from low matching speed. In recent years, latent feature-based template matching methods [6,24,39,40] have achieved real-time performance and have gained popularity. 2D-3D correspondence matching-based methods [37,52] first estimate the 2D-3D correspondences and then retrieve the objects' pose by PnP methods.…”

Section: Related Workmentioning

confidence: 99%

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

Zheng¹,

Wang²,

Sun³

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5 • 2cm metric and 10.3% on IoU 75 . Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5 • 2cm, 6.9% on IoU 75 ) on REAL275 dataset and runs in real-time (50 FPS) 1 .

show abstract

TP-AE: Temporally Primed 6D Object Pose Tracking with Auto-Encoders

Cited by 8 publications

References 49 publications

A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

AG-Net: Category-level 6D Pose Estimation Network Based on Attention Mechanism and Global Enhancement

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

Contact Info

Product

Resources

About