66 citations · 216 across the 50 of their papers we have counts for
9 papers · 2 filters
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
Hanzhi Chen, Fabian Manhardt, Nassir Navab +1
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning s…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Guangyao Zhai, Shun-Cheng Wu +10
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation…
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt +3
Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead t…
CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning
Mahdi Saleh, Yige Wang, Nassir Navab +2
Processing 3D data efficiently has always been a challenge. Spatial operations on large-scale point clouds, stored as sparse data, require extra cost. Attracted by the success of t…
BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks
Shervin Dehghani, Benjamin Busam, Nassir Navab +1
Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition proces…
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Pengyuan Wang, HyunJun Jung, Yitong Li +6
Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape…
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer +5
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replace…
Know your sensORs -- A Modality Study For Surgical Action Classification
Lennart Bastian, Tobias Czempiel, Christian Heiliger +4
The surgical operating room (OR) presents many opportunities for automation and optimization. Videos from various sources in the OR are becoming increasingly available. The medical…
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Mahdi Saleh, Shun-Cheng Wu, Luca Cosmo +3
Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain…