13 citations · 19 across the 8 of their papers we have counts for
6 papers · 1 filter
ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States
Haowen Wang, Xiaoping Yuan, Fugang Zhang +4
Generating articulated assets is crucial for robotics, digital twins, and embodied intelligence. Existing generative models often rely on single-view inputs representing closed sta…
PDGS: Part-Level Decoupling and Continuous Deformation of Articulated Objects via Gaussian Splatting
Haowen Wang, Xiaoping Yuan, Zhao Jin +6
Articulated objects are ubiquitous and important in robotics, AR/VR, and digital twins. Most self-supervised methods for articulated object modeling reconstruct discrete interactio…
SM: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Haowen Wang, Zhen Zhao, Zhao Jin +6
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused o…
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
Haowen Wang, Zhipeng Fan, Zhen Zhao +7
Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric feature…
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Haowen Wang, Zhengping Che, Yufan Yang +6
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent…
RGB-Depth Fusion GAN for Indoor Depth Completion
Haowen Wang, Mingyuan Wang, Zhengping Che +5
The raw depth image captured by the indoor depth sensor usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transpa…