25 citations · 36 across the 7 of their papers we have counts for
7 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…
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…
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning
Weizhe Liu, David Ferstl, Samuel Schulter +3
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation. Unlike many earlier methods that rely on adversarial learning for fea…
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
Inigo Alonso, Alberto Sabater, David Ferstl +2
This work presents a novel approach for semi-supervised semantic segmentation. The key element of this approach is our contrastive learning module that enforces the segmentation ne…
A Deep Primal-Dual Network for Guided Depth Super-Resolution
Gernot Riegler, David Ferstl, Matthias Rüther +1
In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a d…