578 citations · 2.5k across the 216 of their papers we have counts for
150 papers · 1 filter
Visual Autoregressive Modelling for Monocular Depth Estimation
Amir El-Ghoussani, André Kaup, Nassir Navab +2
We propose a monocular depth estimation method based on visual autoregressive (VAR) priors, offering an alternative to diffusion-based approaches. Our method adapts a large-scale t…
UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision
Alberto Rota, Mert Kiray, Mert Asim Karaoglu +4
Specular highlights distort appearance, obscure texture, and hinder geometric reasoning in both natural and surgical imagery. We present UnReflectAnything, an RGB-only framework th…
Language-Guided Open-World Anomaly Segmentation
Klara Reichard, Nikolas Brasch, Nassir Navab +1
Open-world and anomaly segmentation methods seek to enable autonomous driving systems to detect and segment both known and unknown objects in real-world scenes. However, existing m…
SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors
Kunyi Li, Michael Niemeyer, Sen Wang +3
Recent advances in dense 3D reconstruction have demonstrated strong capability in accurately capturing local geometry. However, extending these methods to incremental global recons…
RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
Junwen Huang, Shishir Reddy Vutukur, Peter KT Yu +3
Typical template-based object pose pipelines estimate the pose by retrieving the closest matching template and aligning it with the observed image. However, failure to retrieve the…
OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting
Atakan Topaloglu, Kunyi Li, Michael Niemeyer +3
Sparse-view novel view synthesis is fundamentally ill-posed due to severe geometric ambiguity. Current methods are caught in a trade-off: regressive models are geometrically faithf…
Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
Guangyao Zhai, Yue Zhou, Xinyan Deng +3
Few-shot anomaly detection streamlines and simplifies industrial safety inspection. However, limited samples make accurate differentiation between normal and abnormal features chal…
Visibility-Aware Language Aggregation for Open-Vocabulary Segmentation in 3D Gaussian Splatting
Sen Wang, Kunyi Li, Siyun Liang +4
Recently, distilling open-vocabulary language features from 2D images into 3D Gaussians has attracted significant attention. Although existing methods achieve impressive language-b…
Mitigating Biases in Surgical Operating Rooms with Geometry
Tony Danjun Wang, Tobias Czempiel, Nassir Navab +1
Deep neural networks are prone to learning spurious correlations, exploiting dataset-specific artifacts rather than meaningful features for prediction. In surgical operating rooms…
GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting
Elena Alegret, Kunyi Li, Sen Wang +5
3D scene reconstruction and understanding have gained increasing popularity, yet existing methods still struggle to capture fine-grained, language-aware 3D representations from 2D…
MS-CLR: Multi-Skeleton Contrastive Learning for Human Action Recognition
Mert Kiray, Alvaro Ritter, Nassir Navab +1
Contrastive learning has gained significant attention in skeleton-based action recognition for its ability to learn robust representations from unlabeled data. However, existing me…
Attention Pooling Enhances NCA-based Classification of Microscopy Images
Chen Yang, Michael Deutges, Jingsong Liu +4
Neural Cellular Automata (NCA) offer a robust and interpretable approach to image classification, making them a promising choice for microscopy image analysis. However, a performan…
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
Michael Deutges, Chen Yang, Raheleh Salehi +3
The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell cou…
TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking
Tony Danjun Wang, Christian Heiliger, Nassir Navab +1
Providing intelligent support to surgical teams is a key frontier in automated surgical scene understanding, with the long-term goal of improving patient outcomes. Developing perso…
Forecasting Continuous Non-Conservative Dynamical Systems in SO(3)
Lennart Bastian, Mohammad Rashed, Nassir Navab +1
Modeling the rotation of moving objects is a fundamental task in computer vision, yet extrapolation still presents numerous challenges: (1) unknown quantities such as the m…
Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations
Lennart Bastian, Mohammad Rashed, Nassir Navab +1
Tracking and forecasting the rotation of objects is fundamental in computer vision and robotics, yet SO(3) extrapolation remains challenging as (1) sensor observations can be noisy…
LiteTracker: Leveraging Temporal Causality for Accurate Low-latency Tissue Tracking
Mert Asim Karaoglu, Wenbo Ji, Ahmed Abbas +3
Tissue tracking plays a critical role in various surgical navigation and extended reality (XR) applications. While current methods trained on large synthetic datasets achieve high…
PRISM-0: A Predicate-Rich Scene Graph Generation Framework for Zero-Shot Open-Vocabulary Tasks
Abdelrahman Elskhawy, Mengze Li, Nassir Navab +1
In Scene Graph Generation (SGG), structured representations are extracted from visual inputs as object nodes and connecting predicates, enabling image-based reasoning for diverse d…
Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
Tony Danjun Wang, Lennart Bastian, Tobias Czempiel +2
Surgical domain models improve workflow optimization through automated predictions of each staff member's surgical role. However, mounting evidence indicates that team familiarity…
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments
Ege Özsoy, Chantal Pellegrini, Tobias Czempiel +7
Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assista…
Text-driven Adaptation of Foundation Models for Few-shot Surgical Workflow Analysis
Tingxuan Chen, Kun Yuan, Vinkle Srivastav +2
Purpose: Surgical workflow analysis is crucial for improving surgical efficiency and safety. However, previous studies rely heavily on large-scale annotated datasets, posing challe…
Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples
Samra Irshad, Seungkyu Lee, Nassir Navab +2
The presence of adversarial examples in the physical world poses significant challenges to the deployment of Deep Neural Networks in safety-critical applications such as autonomous…
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Klara Reichard, Giulia Rizzoli, Stefano Gasperini +4
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, cu…
GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
Weihang Li, Hongli Xu, Junwen Huang +4
A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Re…