64 citations · 113 across the 22 of their papers we have counts for
23 papers
Exploring 6D Object Pose Estimation with Deformation
Zhiqiang Liu, Rui Song, Duanmu Chuangqi +3
We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption that fails in practice as objects…
SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
Qingyuan Wang, Rui Song, Jiaojiao Li +3
We introduce SCFlow2, a plug-and-play refinement framework for 6D object pose estimation. Most recent 6D object pose methods rely on refinement to get accurate results. However, mo…
Hierarchical Flow Diffusion for Efficient Frame Interpolation
Yang Hai, Guo Wang, Tan Su +2
Most recent diffusion-based methods still show a large gap compared to non-diffusion methods for video frame interpolation, in both accuracy and efficiency. Most of them formulate…
Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera
Haixin Shi, Yinlin Hu, Daniel Koguciuk +3
We propose an approach for reconstructing free-moving object from a monocular RGB video. Most existing methods either assume scene prior, hand pose prior, object category pose prio…
Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +2
Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their a…
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +1
Most recent 6D object pose methods use 2D optical flow to refine their results. However, the general optical flow methods typically do not consider the target's 3D shape informatio…
Rigidity-Aware Detection for 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +2
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection meth…
NOPE: Novel Object Pose Estimation from a Single Image
Van Nguyen Nguyen, Thibault Groueix, Yinlin Hu +2
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. To addr…
Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation
Fulin Liu, Yinlin Hu, Mathieu Salzmann
Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differe…
Modular Quantization-Aware Training for 6D Object Pose Estimation
Saqib Javed, Chengkun Li, Andrew Price +2
Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D pose…
LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation
Chen Zhao, Yinlin Hu, Mathieu Salzmann
Object location prior is critical for the standard 6D object pose estimation setting. The prior can be used to initialize the 3D object translation and facilitate 3D object rotatio…
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
Shuxuan Guo, Yinlin Hu, Jose M. Alvarez +1
Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely…
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
Van Nguyen Nguyen, Yinlin Hu, Yang Xiao +2
We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these obj…
Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation
Yinlin Hu, Pascal Fua, Mathieu Salzmann
Most recent 6D object pose estimation methods, including unsupervised ones, require many real training images. Unfortunately, for some applications, such as those in space or deep…
Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects
Chen Zhao, Yinlin Hu, Mathieu Salzmann
In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. This task contrasts with the one considered by most existing…
Robust Differentiable SVD
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether u…
Wide-Depth-Range 6D Object Pose Estimation in Space
Yinlin Hu, Sebastien Speierer, Wenzel Jakob +2
6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric sc…
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
Zhigang Li, Yinlin Hu, Mathieu Salzmann +1
While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain…
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square pro…
Single-Stage 6D Object Pose Estimation
Yinlin Hu, Pascal Fua, Wei Wang +1
Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a R…
Backpropagation-Friendly Eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it…
Segmentation-driven 6D Object Pose Estimation
Yinlin Hu, Joachim Hugonot, Pascal Fua +1
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations o…
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector correspond…