24 citations · 34 across the 19 of their papers we have counts for
19 papers
ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs
Yordanka Velikova, Mahdi Saleh, Liming Kuang +1
Modeling continuous object deformation is important for many computer vision and robotics tasks, such as manipulation and simulation. Existing approaches rely on learning-based met…
ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
Liming Kuang, Yordanka Velikova, Mahdi Saleh +3
Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurrently, large-scale vision langua…
PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation
Lei Cheng, Mahdi Saleh, Qing Cheng +4
Despite the advancements in 3D full-shape generation, accurately modeling complex geometries and semantics of shape parts remains a significant challenge, particularly for shapes w…
ESCAPE: Equivariant Shape Completion via Anchor Point Encoding
Burak Bekci, Nassir Navab, Federico Tombari +1
Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known p…
Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation
Oleksandra Tmenova, Yordanka Velikova, Mahdi Saleh +1
Ultrasound imaging is challenging to interpret due to non-uniform intensities, low contrast, and inherent artifacts, necessitating extensive training for non-specialists. Advanced…
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Miruna-Alexandra Gafencu, Yordanka Velikova, Mahdi Saleh +4
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D infor…
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
Mahdi Saleh, Michael Sommersperger, Nassir Navab +1
In robotics, it's crucial to understand object deformation during tactile interactions. A precise understanding of deformation can elevate robotic simulations and have broad implic…
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
Zhiying Leng, Shun-Cheng Wu, Mahdi Saleh +6
Reconstructing both objects and hands in 3D from a single RGB image is complex. Existing methods rely on manually defined hand-object constraints in Euclidean space, leading to sub…
On the Localization of Ultrasound Image Slices within Point Distribution Models
Lennart Bastian, Vincent Bürgin, Ha Young Kim +4
Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US). Longitudinal nodule tracking is a pivotal diagnostic protocol for monitoring changes in patholo…
S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
Lennart Bastian, Alexander Baumann, Emily Hoppe +5
Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domai…
Rotation-Invariant Transformer for Point Cloud Matching
Hao Yu, Zheng Qin, Ji Hou +4
The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rot…
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Hao Yu, Ji Hou, Zheng Qin +5
Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-varian…
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…
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…
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…
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
Hao Yu, Fu Li, Mahdi Saleh +2
We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoi…
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
Mahdi Saleh, Shervin Dehghani, Benjamin Busam +2
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based…
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Stefan Denner, Ashkan Khakzar, Moiz Sajid +4
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cu…
The speaker-independent lipreading play-off; a survey of lipreading machines
Jake Burton, David Frank, Madhi Saleh +2
Lipreading is a difficult gesture classification task. One problem in computer lipreading is speaker-independence. Speaker-independence means to achieve the same accuracy on test s…