578 citations · 2.5k across the 216 of their papers we have counts for
22 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…
SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments
Evin Pınar Örnek, Aravindhan K Krishnan, Shreekant Gayaka +4
Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it di…
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…
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
Azade Farshad, Yousef Yeganeh, Helisa Dhamo +2
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or…
What can we learn about a generated image corrupting its latent representation?
Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic +2
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can tr…
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Ruida Zhang, Yan Di, Zhiqiang Lou +3
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories. Recent methods harness shape pr…
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…
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning
Felix Buchert, Nassir Navab, Seong Tae Kim
The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expen…
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Raheleh Salehi, Ario Sadafi, Armin Gruber +4
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures,…
Virtual embeddings and self-consistency for self-supervised learning
Tariq Bdair, Hossam Abdelhamid, Nassir Navab +1
Self-supervised Learning (SSL) has recently gained much attention due to the high cost and data limitation in the training of supervised learning models. The current paradigm in th…
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…
SoftPool++: An Encoder-Decoder Network for Point Cloud Completion
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not requir…
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis
Mojtaba Bahrami, Mahsa Ghorbani, Nassir Navab
The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled d…
Learning Local Displacements for Point Cloud Completion
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that ar…
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…
4D-OR: Semantic Scene Graphs for OR Domain Modeling
Ege Özsoy, Evin Pınar Örnek, Ulrich Eck +3
Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are…
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
Yan Di, Ruida Zhang, Zhiqiang Lou +4
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their application…
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction
Evin Pınar Örnek, Shristi Mudgal, Johanna Wald +3
There have been numerous recently proposed methods for monocular depth prediction (MDP) coupled with the equally rapid evolution of benchmarking tools. However, we argue that MDP i…
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…
Transformers in Action: Weakly Supervised Action Segmentation
John Ridley, Huseyin Coskun, David Joseph Tan +2
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense f…